School of Engineering and Mathematical Sciences. Development of a Compact Patient Ventilator Based on Novel Compressor Technology

Transcription

1 School of Engineering and Mathematical Sciences Development of a Compact Patient Ventilator Based on Novel Compressor Technology by Mario G. Bejarano M. Project Report for the Degree of MSc in Clinical Engineering with Healthcare Technology Management Supervisor: Dr. Justin Philips Co-supervisor: Dr. Keith Pullen London 14 th of September 2012

2 Table of Contents Abstract Introduction Theory Medical Background Treatment of OSA with Non-invasive ventilation BiPAP, CPAP and APAP devices architecture Market Devices State of the art in CPAP and APAP ResMed S9 TM series Philips System One Transcend Sleep Apnea Therapy System TM Common problems using compact ventilators Problem Identification Methodologies and Techniques Raspberry Pi Python language Motor controller Sensors and ADC Compressor Work done Motor controller design Temperature sensor Pressure sensor Analogue to digital converter ADC Range of operation with the sensor Interfacing sensor, motor control and RasPi I 2 C protocol communication for the temperature sensor SPI communication protocol PWM pin for motor control Algorithm implementation II

3 CPAP Algorithm APAP Algorithm BiPAP Algorithm User interface design with Python Results Relation speed of the motor pressure in close loop Calibration curve of the device Pressure sensor error margin between datasheet and calibration line Range of operation of the device Air flow range Maximum pressure Compressor noise levels Running temperature Power supplies Discussion of results Testing the compact ventilator Raspberry Pi as a tool for projects in clinical engineering Conclusion Suggestions for further work References Appendix A ResMed S9 Series Specifications... A-1 Appendix B CPAP market comparison... B-1 Appendix C APAP market comparison... C-1 Appendix D Schematic design of the compact ventilator... D-1 Appendix E CPAP Algorithm flowchart... E-1 Appendix F APAP Algorithm flowchart... F-1 Appendix G BiPAP Algorithm flowchart... G-1 III

4 Table of Figures Figure 1 CPAP machine with patient interface (16)... 7 Figure 2 Components of a CPAP APAP device... 8 Figure 3 ResMes S9 Series with Humidifier Figure 4 Philips System One with Humidifier Figure 5 Transcend II CPAP Figure 6 Raspberry Pi Model B Figure 7 Raspberry Pi Communication Port schema (32) Figure 8 Half H-Bridge drive configuration for a DC Brush Motor Figure 9 Modified portable vacuum used in the project as compressor Figure 10 MOSFET IRF9Z34N symbol and packing Figure 11 MOSFET STP16NF06FP symbol and packing Figure 12 Half H-Bridge design and components Figure 13 Schematic of the half H-Bridge with circuit protection Figure 14 Pin configuration of temperature sensor TC Figure 15 Schematic temperature sensor Figure 16 Pressure sensor 40PC001B and pin configuration Figure 17 Graph expected linearity pressure sensor Figure 18 MCP3008 Pin configuration Figure 19 Schematic of the pressure sensor - ADC interface Figure 20 Relation between PWM register and output in Volts Figure 21 CPAP mode window Figure 22 APAP mode window Figure 23 BiPAP Mode window Figure 24 Main menu options available Figure 25 Temperature reading mode Figure 26 Pressure reading mode Figure 27 Configuration window Figure 28 Pressure meter RIGEL BP- SiM Figure 29 Relation PWM register versus converted values from pressure sensor Figure 30 Calibration line using external reference Figure 31 Graph real calibration versus expected calibration IV

5 Figure 32 Flow meter Certifier FA Plus Figure 33 Flow graph versus speed of the motor Figure 34 Thermometer Kane-May KM Figure 35 Relation flow versus pressure Figure 36 Setting for testing the compact ventilator's accuracy Figure 37 Error comparison in CPAP mode Figure 38 Figure Error comparison in APAP mode V

6 Table of tables Table 1 American APAP vs CPAP vs BiPAP consumer preference (17).. 10 Table 2 Ideal operation of a half H-Bridge Table 3 Electrical characteristics of the motor Table 4 MOSFET IRF9Z34N features Table 5 MOSFET STP16NF06FP features Table 6 Features temperature sensor TC74A Table 7 Performance characteristics of the pressure sensor 40PC001B.. 30 Table 8 Main features ADC MCP Table 9 Operation range oh the ADC with the pressure sensor Table 10 Equivalent of temperature in HEX values Table 11 SPI Pin assignment on RasPi and ADC Table 12 Correlation calibration line versus pressure sensor expected values Table 13 Test results in CPAP mode Table 14 Test results in APAP mode VI

7 Symbols and abbreviation C: Degree Celsius µa: Micro Ampers A: Amperes ACK: Acknowledge ADC: Analogue to Digital Converter AHI: Apnea/hypopnea index APAP: Automatic Positive Airway Pressure ARM: Advanced RISC Machine BiPAP: Bilevel Positive Airway Pressure BJT: Bipolar Junction Transistor cmh 2 O: Centimetres of Water CO 2 : Carbon dioxide COPD: Chronic Obstructive Pulmonary Disease CPAP: Continuous Positive Airway Pressure CPU: Central Process Unit CS: Chip Select VII

8 CSA: Central Sleep Apnoea CSV: Coma-separated values db: Decibels DC: Direct Current D IN : Slale Serial Data In D OUT : Slave Serial Data Out EPR: Expiratory Pressure Relief GND: Ground GPIO: General Purpose Input/Output GPU: Graphics Processing Unit HDMI: High Definition Multimedia Interfac HEX: Hexadecimal I 2 C: Inter Integrated Circuit IC: Integrated Circuit L/min: Litres per minute LCD: Liquid crystal display VIII

9 LSB: Least Significant Bit ma: mili Amperes MHz: Megahertz MISO: Master Input MMC: Multi Media Card mmhg: Millimetres of Mercury MOSFET: Metal oxide semiconductor Field-effect transistor MOSI: Master Output ms: mili seconds MSB: Most Significant Bit NIV: Non-Invasive Ventilation OS: Operating System OSA: Obstructive Sleeping Apnoea pf: Picofarad PWM: Pulse Width Modulation PWV: Pharyngeal Wall Vibration IX

10 RasPi: Raspberry Pi RCA: Radio Corporation America REM: Rapid Eye Movement RJ: Registered Jack SCL: Serial Clock SCLK: Serial Clock SD: Secure Digita SDA: Serial Data Line SDB: Sleep Disordered Breathing SDIO: Secure Digital Input Output SDRAM: Synchronous Dynamic Random-Access Memory SoC: System on Chip SPI: Serial Peripheral Interface Bus Sps: Smaples per second t e : Expiratory Time t i : Inspiratory Time X

11 UA: Upper Airway UART: Universal Asynchronous Receiver/Transmitter USB: Universal Serial Bus V: Volt(s) XI

12 Project Title: Development of a Compact Patient Ventilator Based on Novel Compressor Technology Student: Mario G. Bejarano M. Supervisor: Dr. Justin Philips Co-supervisor: Dr. Keith Pullen 14 th of September 2012 Abstract There are three different types of compact ventilators for hospital and home users for the treatment of sleep disordered breathing (SDB). These devices are called continuous positive airway pressure (CPAP), automatic positive airway pressure (APAP) and bilevel positive airway pressure (BiPAP). These three types of machines shared common characteristics but in the market there is not a device capable to run the three modes in one machine with portable capabilities. This project endeavours to explore the design and development of a compact ventilator capable of running the three modes in one sole device using a novel compressor and the new Raspberry Pi (RasPI). For this an in depth analysis of the medical background and the current market was performed and some problems are identified for improvement. As a result of this analysis, a prototype for a compressor was designed using the RasPi as the heart of the project and including interfaces to control an external compressor and data acquisition attached to it. The project endeavoured to use a novel compressor but at the moment a scale model is still under development. Therefore, a portable vacuum cleaner was used as a compressor obtaining interesting results which show that this device can run with an accuracy of up to 0.5% for APAP mode. The data collected from the finalised project as a whole gives an interesting platform from which to develop the novel compressor under the suggestions given in section 8. 1

13 1. Introduction There are several illnesses related to sleeping disorders. These diseases are classified as sleep disordered breathing (SDB) (1). If a disorder in sleep time breathing is caused by upper airway (UA) collapse, it is called obstructive sleep apnoea (OSA), but if caused by a lack of neural input from the central nervous system to the diaphragm it is called central sleep apnoea (CSA) (2). There are statistics which show that this illness may affect 4% of males and 2% of females between the ages of 30 to 60 years world-wide (1; 2). Most of the patients must be treated with non-invasive ventilation (NIV) during their sleep time. The length of the treatment will change according to the patient s pathology but it is a long term treatment. The most common medical device used for this purpose is a CPAP device (3). It has been shown that patients do not always require the same level of pressure every night time. As a result, APAP machines have been developed which adjust the pressure value according to the obstruction gravity using intelligent algorithms. Finally, there are some patients who require two different levels of ventilation during the inspiratory-expiratory process, for these cases a BiPAP device is used (4; 5; 6). Modern ventilator devices are designed for home use but there are some common problems that the patients face every night. Including the lack of portability, and the unconformity when using the device. In addition, there is not a machine capable of offering the three types of ventilation in one device, when patients may benefit from such a device and sometimes switch therapies in their treatments (7). The main objective of this project is the design of a compact ventilator capable of running the three different modes of ventilation CPAP, APAP and BiPAP for the treatment of patients with OSA. The compact ventilator should be able to run an external compressor and read the pressure produced from the positive air flow being given to the patient. This design is intended to be used in the future with a novel compressor technology created and designed by Dr. Keith Pullen. This novel compressor has been designed initially for the automotive industry as part of a turbo 2

14 charger engine. However, a new design could be done making the compressor smaller with the characteristics required to operate as a medical device as NIV that could fill the gap available in the market for portable ventilation devices capable to run the three modes in one device. The secondary objective of this project is to evaluate the use of the RasPi as an academic tool for the application in the clinical engineering field. For this, a group of interfaces that connect sensors and an external motor to the RasPi have been created. All this hardware together will be the base to integrate the future compressor designed by Dr. Keith Pullen. The results obtained using the RasPi were quite impressive, considering the cost-benefit relationship of the device. During the development process of this device, an electronic motor control to operate a common brushed 12 volts (V) direct current (DC) motor was designed as well as the electronics required to acquire data from temperature and pressure sensors. In addition, from the software point of view, it was required to develop all the algorithms for the adjustment of the speed of the motor to deliver positive pressure according to the type of treatment. All the algorithms were implemented with programing language Python which is run by the RasPi. Once completed, the device was tested in the workshop of the Medical Equipment Management at King s College Hospital where data was collected to help find the characteristics of the compact ventilator such as range of pressure, air flow, noise levels, temperature and efficiency. With all this collected data, a recommendation for the future design and implementation of Dr. Pullen s compressor is done at the end of the report. 3

15 2. Theory 2.1. Medical Background OSA is mainly characterized by persistent episodes of obstruction of the upper airways during sleep due to pharyngeal wall collapse. When this obstruction occurs the air exchange is reduced completely or partially and most of the time is followed by pharyngeal wall vibration (loud snoring - PWV) (8; 3; 9). This obstruction has physiological effects on the human body. Some of the common symptoms associated with OSA are PWV, daytime drowsiness, fatigue, heart problems and systemic hypertension (2). Abnormal breathing during sleeping can also contribute to the development of hypercapnia 1 when one is awake (3). There are other significant risks associated with OSA such as hypertension, congestive heart failure, coronary artery disease, stroke and arrhythmias (10; 4). In the case of CSA, the apnea is originated mainly by neuronal causes during the rapid eye movement (REM) sleep cycle. REM is associated with sleep alterations especially inhibition of alpha and gamma motor neurons (3). This means that during this cycle the rib cage muscles, intercostal muscles, postural muscles and respiratory muscles reduce their contribution to ventilation. Hence, causing hypotonia or diminished muscle tone. As a consequence, the respiration during REM sleep cycle becomes highly dependent on the diaphragm s effort and not on the action of the rib cage and intercostal muscles (3). In addition, the lack of activity of the ventilation muscles means that they cannot maintain the end-expiratory pressure to prevent small airway closure. As a result, the ventilation-perfusion relationship is reduced, worsening gas exchange in the patient and efficiency of breathing. Hypoventilation is the common term to describe the lack of gas exchange caused either by obstruction of the upper airways or by hypotonia of the ventilation muscles. This in turn causes an increase of carbon dioxide (CO 2 ) 1 Condition indicating that there is too much carbon dioxide (CO 2 ) in the blood. 4

16 (hypercapnia) in the body during REM sleep. This level of CO 2 could be detected and monitored continuously during the evening using a pulse oximeter or transcutaneous methods (3). The development of technology has helped to better our understanding of breathing during sleep. The following devices have contributed to the development of the research of sleeping disorders. The use of the pulse oximeter has helped to continuously monitor the level of oxygen during the sleeping cycle. The development of a nasal mask interface that is comfortable and acceptable has been an effective, non-invasive way to treat breathing abnormalities. Lastly, yet another example of devices contributing to the research of sleeping disorders has been the development of portable ventilation devices that can even be used at home (3). Some patients who are candidates to use non-invasive ventilation (NIV) technologies for the treatment of their illnesses include patients with any of the following: Kyphoscoliosis: musculoskeletal disorder caused by abnormal curvature of the spine which causes chronic under ventilation due to low chest compliance. Cystic fibrosis: Autosomal recessive genetic disorder compromising mainly the lungs but also in some patients can affect the pancreas, liver and intestine (11). It has been recognized that the use of nocturnal ventilation shows beneficial effects in these kinds of patients. It must be pointed out that low CO 2 retention is a consequence of this disease because of the chronic hyperventilation that the patient suffers. As a consequence, if oxygen is also applied during therapy it could promote CO 2 retention which is beneficial in these patients. (3; 12). Duchene muscular dystrophy: It is a genetic illness which could lead to muscle degeneration, difficulty in breathing and walking. It has been shown that the use of long-term non-invasive ventilation could help to stabilise pulmonary function. Hence, prolonging life expectancy of the patient (13). 5

17 Chronic obstructive pulmonary disease: COPD is the limitation of the flow of air from/to the lungs by narrowing of the bronchi and bronchioles. This disease can affect the respiratory airways, the lungs, parenchym 2 and the pulmonary circulation (14). The use of noninvasive nocturnal ventilation has shown beneficial results mainly in patients with severe hypercapnic COPD by blowing off the extra CO2 and optimising gas exchange (3; 15). Motor neurone disease: is a neurological disorder that affects the motor neurones. The use of NIV is especially useful when respiratory insufficiency occurs in the late manifestation of this disorder because respiratory muscles and global peripheral weakness decrease the effectiveness of the patients breathing which is then improved with the NIV therapy (3). Obesity hypoventilation syndrome: This condition presents in overweight people. People with this condition usually fail to breathe deeply or quickly enough to maintain a healthy level of oxygen in the blood. The use of NIV has been shown to give a rapid and positive response in this group of patients (3) Treatment of OSA with Non-invasive ventilation The treatment of OSA with NIV therapy is a ventilation technique whereby positive pressure is applied to the lungs through the upper airways, without the need for an endotracheal or tracheostomy tube. The aim of this therapy is to improve the gas exchange by supporting the inspiratory effort and reducing the labour of breathing (3; 14). This device is manly used to manage acute or chronic upper airways obstructive disorders especially when the patient is sleeping. The use of this type of device during sleep prevents hypoventilation and may help to restore sensitivity to CO 2 and hence increase the drive to breath (3; 14). There are two different types of NIV systems: volume-preset and pressurepreset (3). The volume-preset ventilators deliver a fixed tidal volume 2 It refers to specializes organ tissue in the respiratory system 6

18 regardless of the airway pressure generated using a time-cycled flow generator. In contrast, the pressure-preset systems work delivering continuous positive pressure to the patient to the pre-set value. There are three types of these devices: the CPAP, APAP and BiPAP devices. The BiPAP devices deliver positive pressure at two different levels. Tidal volume will vary according to the set inspiratory pressure, the difference between inspiratory-expiratory pressure and chest wall and lung compliance of the patient (3). The CPAP/APAP delivers compressed air continuously to the patient s pharyngeal airway at a preset or sense pressured value. This value is set with the aim to act as a pneumatic splint which opposes airway collapse (2; 10; 5). The most common interface used between the device and the patient is a corrugated PVC tube and a specially designed mask (See Figure 1). This is why this method of ventilation is also known as mask ventilation (3). Most of the problems with ventilation are related to the mask, due to leaks or patient discomfort and hence non-compliance with the treatment. If the mask does not fit properly and leaks occur around the mask, this will reduce the effectiveness of the treatment (10). Figure 1 CPAP machine with patient interface (16) 7

19 2.3. BiPAP, CPAP and APAP devices architecture Nowadays, BiPAP, CPAP and APAP are the most common treatments of OSA and hypopnea. It has been demonstrated that this therapy relieves daytime sleepiness, improves driving performance, mood and the patient s overall quality of life (9; 10; 4). BiPAP machines deliver positive pressure at two different levels, inspiratory and expiratory. As a result, tidal volume varies according to the set inspiratory pressure, the difference between inspiratory-expiratory pressure and chest wall and lung compliance of the patient during sleep (3) On the other hand, CPAP machines continuously deliver the therapeutic pressure while the patient is sleeping with the aim of keeping the UA open. However, it has been proved that patients do not always require the same level of pressure every night time. As a result, APAP algorithms have been developed to deliver an auto-set pressure value according to the obstruction severity (4; 5; 6). User control User Interface Compressor Storage CPU AC/DC Converter Pressure Sensors External or Internal power pack External Battery Pack Figure 2 Components of a CPAP APAP device 8

20 All the NIV devices have the following architecture in common (See Figure 2). Including firstly, a central process unit (CPU) which is the brain of the device and performs the control and arithmetic calculations of the algorithms required to adjust the pressure needed by the patient. Some devices also include a storage port, which usually is a secure digital (SD) card. Secondly, a compressor which generates the pressure necessary for the treatment by air flow regulation is required. Thirdly, the analogue to digital converter (ADC) which is the interface between the sensors (temperature, flow and/or pressure) and the CPU is also part and parcel of the typical NIV architecture. As part of the device s set up, modern compact ventilators include user s interface. More modern devices now include data analysis and for this, storage devices have been added to the design. Most of them use SD storage cards for recording events and patients history Market Devices The market of compact ventilators is very competitive. In basic terms there are two segments, trusts and consumer markets. In the UK most of the equipment is bought by the trusts and some are given to the patients and others are used in respiratory wards. However, in the American market consumers tend to buy their own devices. The companies which traditionally have developed and designed these devices are: ResMed (ResMed, Bella Vista, NSA, Australia), Philips Respironics (Philips Electronics, Murrysville, Pennsylvania, USA) and Fisher and Paykel (Fisher and Paykel, East Tamaki, Auckland, New Zealand). However, new companies have been developing new economical devices that are popular between American consumer segments. This is the case of PMI Probasics (PMI Probasics, Marlboro, New Jersey USA), Somnetics (Somnetics International Inc., New Brighton, Minnesota, USA), DeVilbiss (DeVilbiss Healthcare, Somerset, Pennsylvania, USA) and Viasys (CareFusion Corporation, San Diego, California, USA). 9

21 Rank Individual Machine % of Orders Q Average Price Q % of Orders Q Average Price Q % Share Change 1 CPAPs 52.3% $ % $ % 2 APAPs 45% $ % $ % 3 BiPAPs 2.7% $ % $ % Table 1 American APAP vs CPAP vs BiPAP consumer preference (17) Table 1 shows the American consumer preference for CPAP vs APAP vs BiPAP over third quarter Q3 (Jul-Aug 2012) (17). In this table it can be appreciated that consumers are selecting more CPAP machines than APAP and the percentage of change is not very considerable in comparison to the Q2. According to the analysis done by cpap.com the lower price of CPAP machines may contribute to this trend. However, it suggests also that the device Trascend II (Somnetics International Inc., New Brighton, Minnesota, USA) could have influenced this trend because of its innovative size and portability. What can be also noticed on the table is the price difference between the three devices. BiPAPs are considerably more expensive than APAPs (about 200% more expensive). In comparison to the APAPs which are on average 187% more expensive than common CPAPs State of the art in CPAP and APAP The traditional companies are the ones that are always innovating and bringing new features to its products. The following analysis shows what are latest state of the art devices and the unique characteristics of each device. The smallest device available in the market will also be analysed. The Appendix B shows a comparison of the CPAP market and Appendix C shows a market comparison for the APAP. 10

22 ResMed S9 TM series The S9 TM series is the latest model of CPAP and APAP released by ResMed in Europe in February 2010 (18). The main key points of the device are: optimal climate control (temperature compensation for the pressure), reduced noise levels with the Easy-Breathe motor technology and also adaptation to the patient s breathing cycle with the enhanced Expiratory Pressure Relief (EPR) technology. The model S9 Autoset differentiates between obstructive and central apnoea events and adapts accordingly. It has been designed with simplified and intuitive interface. This model can store information, notably a summary of compliance and efficacy data for up to 1 year on an SD-Card, including mask leak, apnea/hypopnea index (AHI) and central apnea statistics. The temperature and humidity are controlled by five different sensors achieving optimal humidification automatically when it is used in conjunction with the humidifier H5i (18). There are three different models available on the market. The model S9 Escape TM is a CPAP device focused mainly to treat OSA. The model S9 Elite TM is also a CPAP machine which treats OSA but includes additional features such as EPR. The S9 AutoSet TM is an APAP device that adjusts pressure throughout the night, this device is recommended for patients with OSA or CSA. It includes all the high end characteristics such as EPR (19). For full specifications of this device see Appendix A. Figure 3 ResMes S9 Series with Humidifier 11

23 Philips System One Due to the acquisition of Respironics by Philips in 2007 the product family is stopping to be branded as Respironics (20). The new family of products that Philips is offering is called System One sleep therapy platform. The main characteristics of this product are: use of advanced software algorithms which track 30 days of sleep apnoea progress and adapts to patient s needs keeping the patient informed. This is not an APAP device because it fixes the pressure of therapy and adjusts every 30 hours. Philips also claims that is the quietest device on the market with the WhisperSmart technology (21). In terms of humidification, it provides two types of humidification, auto-controlled and patient controlled. The system provides the technology System One Resistance Control which instructs the device to compensate any variable resistance characteristic related to different masks (22). It also includes C-Flex Pressure Relief technology for patient s breathing comfort; this is an adaptive control system which relieves pressure at the beginning of exhalation and returns to therapeutic pressure just before inhalation. This is possible due to Philip s patented Digital Auto-Track & Sensitivity algorithm (23). Figure 4 Philips System One with Humidifier As part of the software innovation, the System One provides the robust REMstar Auto algorithm which determines proactive performance for optimal therapy levels, comprising of: leak management, detection of obstructive apnea or hypopnea, flow limitation where the algorithm analyses changes in 12

24 roundness, shape, peak and flatness, as well as, snore detection. This last feature has been recognized as having the best reaction to changes of flow in the market (24) Transcend Sleep Apnea Therapy System TM This is one new competitor on the market that has visible differences compared to the rest of the market. This is a CPAP device that has been catalogued as the smallest CPAP in the world and an innovative humidifying system. Some of the key benefits of this device are that its size makes it highly portable, the water free humidifier is hassle free and the use of an external battery power pack makes it very convenient for outdoors travellers. In fact its low weight makes it highly portable. The technology behind the CPAP does not show any innovations related to pressure relief while breathing out or algorithms that improve the therapy or compliance (25). However, it includes software that must be installed in a computer to see the previous night s treatment, potential problems with apnea events and mask leaks. The only technological advances that this product offers compared to the leaders is the fact that it is highly portable due to extra power options available that can run from 5 8 hours (26). The only differentiator that this device offers is the innovative waterless humidification system. To achieve this, they developed a heat moisture exchange (HME) technology. This is a breakthrough in humidification technology because it does not require water, electrical heating elements and it is disposable. Figure 5 Transcend II CPAP 13

25 2.6. Common problems using compact ventilators Most of the problems related to CPAP/APAP/BiPAP treatment are related to the mask. A poor fit of the mask on the patient could cause a low compliance in the treatment. Mayo Clinic has identified the 10 most common problems related to these devices and possible solutions (7). The wrong CPAP mask size or style and no tolerance to the mask at the beginning of the treatment are the most common problems when using CPAP machines especially with new patients. It is recommended to find the most suitable mask for the patient and start to use the device in common activities such as watching TV or cooking. Mayo clinic recommends the use of a full face mask that covers the mouth and nose attached with straps for good grip. The nasal pillows are also recommended by Mayo clinic, these feature a nostril fitting with straps around the forehead to secure it (3; 7). The patient s ability to tolerate the forced air as well as the difficulty in sleeping whilst using this device are the second and seventh most common problems respectively. For this, it is recommended to use the ramp function that is included in most of the machines or use bilevel machines. The ramp function usually starts at a low level pressure and the pressure increases in small steps during certain periods of time until it reaches the therapeutic pressure (7). Philips and ResMes have solved this problem by adding C-Flex and EPR algorithms to their CPAP devices (18; 23). The fourth problem recognized by Mayo is a dry or stuffy nose. Overcoming this problem could be done by adding humidification. Most of the devices have an optional humidifier to be attached to their machines (See Appendix A CPAP market comparison) (7). The fifth problem is claustrophobia or panic attacks. Some of the masks fit around the nose and mouth making patients feel claustrophobic which can worsen when patients are given high pressure air flow. This can be solved by selecting the most appropriate mask to the patient or changing the patient to bilevel treatment (7; 27). 14

26 The sixth common difficultly is leaky masks, skin irritation or pressure sores. First of all, a leaky mask indicates that the patient is not getting the full treatment. Secondly, it could release air into the patient s eyes, causing irritation or conjunctivitis. This is one of the most distressing difficulties to patients. This can be solved by changing the mask to another one with a better fit or the use of cloth masks which alleviate this problem (7; 27). A dry mouth is the eighth most common problem. This usually happens when patients fall sleep with their mouth open and air comes in. A chin strap or a full face mask could help to cure this problem. Another recommendation is the use of an oral appliance which helps to lower the severity of the upper airways occlusion (7; 27). The ninth common problem is accidentally removing the CPAP/APAP machine while sleeping. During night time some patients tend to move a lot, as a consequence they can unintentionally remove their masks. There is not a solution for this issue, sooner or later this will occur during the patient s treatment. However, Mayo clinic recommends setting alarms in the night to remind the patient to check if the mask is still in position (7; 27). The last common problem related to the use of CPAP/APAP/BiPAP is when patients or their partners are annoyed by the noise of the device. Nowadays, most of the machines are very quiet with noise levels less than 30 dba. Nevertheless, if a filter is blocked it might cause the machine to overrun and make more noise. This can be solved by regularly servicing the machine (7; 27) Problem Identification According to the information and the background found, the following details to be improved can be deduced. 1. All the manufactures claim that their products are portable. However, there is no truly portable equipment that can run with internal batteries. Most of the commercial equipment require between 3.0 and 5.0 amperes (A) to run its motors. The only equipment that could fulfil this 15

27 requirement is the Transcend II but this device uses an external battery pack. 2. It is clear that the Transcend II is for now fulfilling the niche in the market for a portable device and it is becoming popular according to the statistics shown. Nonetheless, in the APAP and BiPAP field there is no such device that could bring the portability of the Transcend II and the software algorithm to create a smart device. 3. It can be seen that there are 3 different products to cover the market of compact ventilators. There is not a product capable to fulfil CPAP/APAP/BiPAP functions in one machine. This means that there is an increase in costs of hospitals and home patients when they need to swap to a different ventilation mode. This research has pointed out that there are still some areas of improvement in the NIV market of compact ventilators. The need and niche in the market of a portable device capable to work in all three modes CPAP, BiPAP and APAP has been highlighted. In the following sections the design of a prototype capable of meeting this niche market and suggestions to solve the portability issue will be discussed in detail. 16

28 3. Methodologies and Techniques A wide range of electronic tools and software were used in the design and development of this project. The basic components of the compact ventilator designed were based on the parts described in Figure 2 of section 2.3 (BiPAP, CPAP and APAP devices). Creating a compact ventilator capable of running CPAP/APAP/BiPAP modes was divided into different sections where hardware and software were primarily required. The hardware design included motor controllers, ADC and interfaces to transmit data/commands in and out the RasPi. The software implementation was aimed to process the data collected from the sensors and then control the speed of the motor according to the pressure set. Once the theory of all the components integrated in the design of this project have been explained, the operation of all the components together including the implementation of the circuits and algorithms will be described in section 4. (Work done) Raspberry Pi The RasPi is an ultra-low-cost credit-card size Linux computer (28) created mainly with purpose of teaching children how to program computers. This device was developed by the Raspberry Pi foundation (Raspberry Pi Foundation, Cambridge, UK, Registered charity Number ) which is a charity which aims to promote computer science and related topics particularly at school level. Figure 6 Raspberry Pi Model B 17

29 There are two different models of RasPi available in the market currently, the model A and model B. For the present project model B was used, this device has a price of (29) and can only be bought through Farnell and RS- Components websites. This device and the particular model were selected for the development of the project because it had all the tools required to achieve the objectives of the assignment. The RasPi includes peripherals such as communication ports with standard electronics protocols and high level programming language capable to execute instructions required by the algorithms. This device requires additional components that must be bought separately such as: an SD card, keyboard and mouse, external mini USB adapter, and cables HDMI or video composite RCA. This device comes equipped with the following characteristics (30): SoC Broadcom BCM2835 (CPU, GPU, DSP, and SDRAM) CPU: 700 MHz ARM1176JZF-S core (ARM11 family) Videocore 4 GPU Memory (SDRAM) Onboard storage/ Storage via: SD, MMC, SDIO card slot 10/100 Ethernet RJ 45 on-board network Another advantage of using this device is that it can run Linux and the programming language Python. This means that the device is able to perform several mathematical calculations and give results practically instantly due to its 700 MHz processor. The image of the operating system used in the assignment was Debian Linux with kernel 3.2 ( r1 built on ) compiled by Chris Boot (31). This version of kernel was selected over the official Raspbian Wheezy release, because it natively supports the communications protocol inter integrated circuit (I 2 C), serial peripheral interface bus (SPI) embedded in the chip and the pulse width modulation (PWM) module. Another advantage of using an open-source operating system and programming languages is that continuously new tools and libraries are being 18

30 developed which could continuously improve the device. For instance, a web server could be installed in the device making the device a candidate for telemedicine projects. However, one of the disadvantages of using the RasPi was that is still a new product and not much academic information or books are available. Most of the information found was on the internet and come from forums or hobbyists making projects with it. Consequently, it was quite time consuming trying to tune in the RasPi for this particular development. Figure 7 Raspberry Pi Communication Port schema (32) The Raspberry Pi has a 26-pin general purpose input/output (GPIO) expansion header (See Figure 7) (32). It provides 8 GPIO pins including access to I 2 C (GPIO 0 GPIO 1), SPI (GPIO 7, GPIO 8, GPIO 9, GPIO 10 and GPIO 11), universal asynchronous receiver/transmitter (UART GPIO 14 and GPIO 15) as well as +3.3 V (PIN 1), +5 V (PIN 2) and ground (GND 19

31 PIN 6) supply. A special protection circuit was required to shield the RasPi from short-circuits or over-charges because its GPIO does not support voltages over +3.3 V and can just supply a few millivolts per pin. In addition, the RasPi does not include an ADC to connect sensors. For this reason, an external ADC operating at the same voltage level was required. In addition to the special communication options explained previously, the GPIO 18 also supports an alternative function such as pulse width modulation (PWM). This option was particularly useful during the project because it allowed the RasPi to directly control the speed of the motor using the PWM; section 4.1 will go into more depth as to how this function was used Python language As previously mentioned, one of the advantages of using a RasPi is that it runs the Linux operating system (OS) using an SD card. There are different versions of this OS with different types of add-ins that could be used for any specific application. It also runs different types of high level programming language running over Linux. RasPi has tested several programming languages such as Python, Java, PHP and Groovy. It also expects to work other several options of programming language (33). Python was selected as the programming language to implement the algorithms. This programing language is similar to C++ but it is easier to learn and implement. This programming language is free to use because of its open source license (34). There are two different versions available of Python the 2.6 and 3.0. This project is based on the version 2.6 because it has several libraries available on the internet and it is also the most stable version Motor controller The motor controller is one of the most active parts of the circuit. This requires a group of transistors switching constantly and working in the saturation region. As a result, the use of transistors capable of handling a high volume of current and dissipation of energy was required. 20

32 There are as many different types of motor controllers as there are motors (35). The portable vacuum cleaner is a common 12 V DC brushed motor with a maximum consumption of 3.2 A. To generate positive airflow the motor only needs to rotate in one direction. Thus, a half H-Bridge circuit was the most suitable circuit to be implemented (Figure 8). Enable Q1 Vsupply Motor PWM Q2 Figure 8 Half H-Bridge drive configuration for a DC Brush Motor As can be seen in Figure 8, the transistor Q2 is using a PWM signal as a voltage regulator. The operation mode for the half H-Bridge using transistors can be explained as follows: firstly, to operate the motor it is necessary to apply voltage to the positive pin of the motor. For this purpose, the transistor Q1 which is connected to the positive power supply is activated by applying a voltage to the input pin. Secondly, the motor requires a differential of potential between its two terminals to control the voltage required by the motor. For this reason, the input of the transistor Q2 is connected to a PWM generator. When the PWM signal is generated the voltage output of the transistor Q2 will vary according to the voltage applied to the input. If a voltage equivalent to Vsupply is applied to the input of Q2, the output will be Vsupply not allowing current to go through the motor. However, if a voltage less than Vsupply is applied to the input, this will create a differential of potential in the motor which will activate the operation of the motor. 21

33 In theory, a half H-Bridge would work ideally according to Table 2. Yet, a brush motor needs a minimum voltage to activate the motor. In the development of the project, the minimum voltage to operate the motor as well as the equations required to operate the motor were found. Q1 Q2 Result 0 0 Motor not enabled Vsupply/2 Motor enabled to operate. 0% of speed of the motor. Motor enabled operating 50% of maximum speed. 1 Vssupply Motor enabled operating maximum speed. Table 2 Ideal operation of a half H-Bridge 3.4. Sensors and ADC The design of a compact ventilator requires different types of sensors according to the application or the complexity of the device. The most common sensors used are temperature, pressure, flow and tachometer. The temperature sensor is commonly used to read the ambient temperature and perform adjustments in the pressure sensor according to the readings obtained. The pressure and flow sensor are constantly sensing the output of the compact ventilator and the air flow coming into the compressor. With this information it is possible to determine whether the therapy its being properly delivered or if there is a leak or fault in the system. Tachometers are used to constantly check the function of the motor in the compressor. If there is any mismatch between the pressure and the speed of the motor, this information could be used to identify possible faults in the device. All the features mentioned previously cannot be possible with a proper method to read these physical variables and making adjustments when the device is in use. This is only possible by incorporating an ADC to the system. All the analogue signals from the different sensors are converted into digital signals using this type of integrated circuit. All this data collected is then 22

34 processed by the main processor to make adjustments or verify the optimal operation of the compact ventilator. The use of the ADC in the project will be further explained in section (Analogue to digital converter) Compressor The compressor is one of the key features when designing a compact ventilator. The compressors available in the market for CPAPs/APAPs/BiPAPs are high-tech devices that can deliver high air flow and hence a great pressure can be created in the patient s UA. Modern ventilators include very quiet motors that can run in the order of decibels (dba), some of them run relatively cold and deliver therapeutic pressure from 4 20 centimetres of water (cmh 2 O). The energy consumption of these devices varies from A according to their power supplies with an input voltage of VDC. In the inspection of the awarded motor of the S9 Elite CPAP Dr. Keith Pullen pointed out some improvements that can be done in terms of the design of the compressor. Dr. Pullen s compressor is being implemented in other nonautomotive applications; this project looks forward to implement a scale model into the medical industry. At the end of this document, the recommendations done of the desirable characteristics of the motor for a compact ventilator will be described. Due to not having the prototype of Dr. Pullen s compressor at this stage; a commercial compressor was used in the project. Different types of compressors were tested but the only one capable to produce the air flow required to induce enough pressure for testing was a portable vacuum cleaner that runs at 12 VDC as shown in Figure 9. This compressor as expected is not designed to generate positive flow, therefore modifications in its case were performed such as sealing all the vents and inserting an air flow outlet on the side. The results obtained from this 9 device were truly 23

35 amazing and provided valuable information for the design of a future compressor 3. Figure 9 Modified portable vacuum used in the project as compressor 3 Commercial compressor bought on ebay in the following link m1439.l2649#ht_4440wt_

36 4. Work done The application of the theories and circuits explained before will be described in the present section. The Appendix D Schematic design of the compact ventilator shows the full schematic of the device. The development of the project was divided into the following sections: 1. Hardware design, selection of components and sensors to be used within the device. 2. Raspberry Pi tuning and programing of the algorithms using Python. 3. Calibration and results Motor controller design As explained in section 3.3 Motor controller, a half H-Bridge was used in the design. The motor characteristics were used to determine the right components to be used. The packaging of the vacuum did not have any electrical description of the motor but the operating voltage was described on the boxing. However, it was necessary to find the current consumption of the device which was not described. This value was essential for the selection of the proper transistors. Consequently, to find this value an experiment was run using a switched power supply with an ammeter display on the front. The motor was connected directly to the power supply and the 12 VDC were applied directly. Using the current limitation knob, this was adjusted until the maximum consumption of current was found. The following table describes the electrical characteristics of the motor. Characteristic Voltage Maximum current Value obtained 12 VDC 3.2 A Table 3 Electrical characteristics of the motor As portrayed in Table 3, the design of the half H-Bridge should be able to drive at least 3.2 A at 12 VDC. The most suitable device to run over this high 25

37 demand of current is a metal oxide semiconductor field-effect transistor (MOSFET) (35). Another advantage of using this type of transistor is that the gate is driven by voltage and not by current, such as with the common bipolar junction transistor (BJT) which will also protect the RasPi in case of any overload or short-circuit. The MOSFET selected for this purpose was the p-channel type, which acts as the enable gate (Table 4 and Figure 10 illustrates the most significant features). On the schematic of Figure 12 this can be identified as the Q1. The transistor Q2 (As on Figure 12) is an n-channel STP16NF06FP 4 which will be doing the switching coming from the PWM. The features, symbol and packing are shown in Table 5 and Figure 11. Symbol Parameter Max Units I p Continuous Drain Current -19 A V GS Gate to Source Voltage ±20 V V DSS Drain to Source Breakdown Voltage -55 V Table 4 MOSFET IRF9Z34N features Figure 10 MOSFET IRF9Z34N symbol and packing Symbol Parameter Max Units I p Continuous Drain Current 16 A V GS Gate to Source Voltage ±20 V V DSS Drain to Source Voltage 60 V Table 5 MOSFET STP16NF06FP features 4 Datasheet available at 26

38 Figure 11 MOSFET STP16NF06FP symbol and packing As part of the design capacitors (10 picofarad pf) were required for filtering the back noise coming from the motor. Diodes (1N4005) were added on the drains-source pins to improve the response of the MOSFET. Figure 12 presents the schematic of the design and components of the half H-Bridge. 1N pf Q1 IRF9Z34N 12 VDC Motor Q2 STP16NF06FP 1N pf Figure 12 Half H-Bridge design and components Figure 13 Schematic of the half H-Bridge with circuit protection 27

39 A buffer was required for additional protection against short-circuiting or overloading on the RasPi. For this purpose a buffer microchip TC4428A 5 was used as shown on the schematic above. This integrated circuit (IC) works as an amplifier by increasing the voltage coming from the RasPi and putting it on the level of the power supply Temperature sensor The temperature reading is required in the device to compensate variations that this physical variable could cause to the pressure measured. A temperature sensor that works over protocol I 2 C was used in the circuit design. The reason why this type of sensor was selected is because it includes its own 8 bits ADC and directly sends the readings of the temperature over the protocol. Figure 14 Pin configuration of temperature sensor TC74 The temperature sensor used is the microchip TC74A5 6 (See Figure 14). As can be seen from Table 6, this device operates in a range of 5 V and gives and acceptable accuracy of 2%, enough for the application requirements. The resolution is 1 degree Celsius ( C) and the conversion rate is a nominal 8 samples per second (sps). Symbol Parameter Max Units V DD Supply Voltage +6 A I DD Operating Current 350 µa 5 Datasheet available at 6 Datasheet link 28

40 Temperature to bits converter T ERR Temperature Accuracy TC47A +2 C CR Conversion rate 8 Sps Table 6 Features temperature sensor TC74A5 Detailed explanations of how I 2 C, protocol and addresses are set to obtain the readings from the sensor are explained in section I 2 C protocol communication. The schematic of the pin connection to the RasPi are shown in the following figure. Figure 15 Schematic temperature sensor 4.3. Pressure sensor The compact ventilator requires an operation pressure in the range of 0 20 cmh 2 O which have been found to be the most common therapeutic pressures. For this reason, using a pressure sensor to work within this pressure specification was required. The pressure sensor selected was the Honeywell 40PC001B 7. This sensor provides a low consumption and a quick response required for the design. It also provides a wide operation range 7 Datasheet link 29

41 from -50 millimetres of mercury (mmhg) to +50 mmhg and the zero point is centred at the middle of the scale. All the details are shown in the table below. Characteristic Min. Typ. Max. Units Operating pressure mmhg Sensitivity 40 mv/mmhg Zero point offset Output at -50 mmhg 0.5 V Output at +50 mmhg 4.5 Current consumption 10 ma Response time 1 ms Table 7 Performance characteristics of the pressure sensor 40PC001B Figure 16 Pressure sensor 40PC001B and pin configuration According to the information found on the datasheet and shown in Table 7, a calibration line can be plotted taking as reference the voltage output at -50 mmhg (0.5 V), +50 mmhg (4.5 V) and 0 mmhg (2.5 V). According to this, it is expected that the sensor would have an ideal calibration line as shown in Figure 17. It is apparent form the graph below that a linear response is expected from the pressure sensor. 30

42 mmhg y = 25x R² = Volts Pressure sensor calibration line Linear (Pressure sensor calibration line) Figure 17 Graph expected linearity pressure sensor In order to identify the characteristic equation of the calibration line, the linear regression of the graph was performed. Thereby, calculating Equation 1 which represents the response of pressure versus voltage. Equation 1 Calibration line of the pressure sensor It must be noted that the characteristics given by the datasheet are represented on the scale of mmhg. However, the scale used in clinical terms is cmh 2 O. For a better understanding of the project all the values will be expressed in both scales were cmh 2 O should be the pressure to be considered for clinical tests Analogue to digital converter To process the readings coming from the pressure sensor, it was necessary to convert the analogue signals into digital values for further processing in the RasPi. The ADC selected for the project was the Microchip MCP This IC is an ADC operated by SPI serial interface (modes 0,0 and 1,1) which can be connected directly to the SPI pins of the RasPi as is explained in section SPI communication protocol. 8 Datasheet available at 31

43 Some of the highlighted features of the device are that it has 8 channels available and it has a 10-bit resolution which is enough to connect the pressure sensor to it. Within the specifications described on the datasheet, it was also found that the ADC use a sample rate of 75 ksps which is quick enough for the application of the compact ventilator. Table 8 shows the general characteristics of the ADC. Symbol Characteristic Min. Typ. Max. Units t conv Conversion Time 10 Clock cycles t SAMPLE Analogue Input Sample Time 1.5 Clock cycles Resolution 10 Bits V DD Operating voltage V I DD Operating Current µa f CLK Clock frequency MHz Table 8 Main features ADC MCP3008 Figure 18 MCP3008 Pin configuration The ADC was powered to the +3.3V of the RasPi which provided enough current and voltage to operate de ADC. The following figure shows the schematic of how the pressure sensor and the ADC were connected and its interface to the RasPi. 32

44 Figure 19 Schematic of the pressure sensor - ADC interface ADC Range of operation with the sensor The ADC was set to use as voltage references the lines +3.3V and GND. With these levels of reference the resolution of the system can be determined. Finding, the resolution of the ADC with these values is possible by using Equation 2. Equation 2 Resolution ADC The minimum resolution of the ADC is 3.2 mv which is enough to detect any change in the pressure sensors. According to the datasheet of the ADC (see Table 7) the sensitivity of the pressure sensor is 40 mv/mmhg. This means that the ADC will be able to detect any changes as small as 0.08 mmhg (0.11 cmh 2 O) per change in the least significant bit (LSB) according to the following equation. Equation 3 Minimum pressure resolution As can be seen in the equation above, this result shows that the sensor with the ADC provides a good response to minimum changes of pressure. It is very sensitive for the application of the compact ventilator. 33

45 The range of operation of the pressure sensor can be found by delimiting the maximum capability of the ADC conversion. For this, the operational range of the positive pressure of the sensor is found by the following assumptions. Firstly, the pressure sensor is powered to a 5V source as shown in Table 7. It is expected that the zero point is found at 2.5 V as per calibration line in Figure 17. The compact ventilator will be delivering positive pressure to the patient; this means that the effective range will be in the positive side of the calibration line. In other words, the operation range will be between 2.5 V (0 mmhg) and 4.5 V (50 mmhg). However, the ADC is using as voltage references +3.3V and GND. This means that the operational range will be limited to a maximum of +3.3V of the input of the pressure sensor. With these values it is possible to find the operational range of the sensor with the ADC. From Equation 1, we can find which will be the maximum pressure read by the system at +3.3V. Equation 4 Maximum pressure that the system can read According to this, the range of the ADC with the sensor is from 0 20 mmhg ( cmh 2 O). The Table 9 illustrates the whole range of values found in different scales. From this information, it can be deduced that the pressure sensor configuration is more than optimum for the application of the compact ventilator. Characteristic V mmhg cmh 2 O ADC value (DEC) Maximum Reading Zero Point Table 9 Operation range oh the ADC with the pressure sensor Now the theoretical digital output value produced by the ADC connected to the pressure sensor is a function between the voltage produce by the transducer in the sensor and the voltage of reference of the circuit. Equation 34

46 5 found in the ADC s datasheet provides an illustration of how to find the expected decimal values from the pressure read from the pressure sensor. Equation 5 Digital output code equation from ADC According to this, the digital range when the sensor is reading from 2.5 V to 3.3 V is 776 and 1024 in decimal values respectively. The Table 9 shows the sensor range and its representation in decimal value after the ADC s conversion Interfacing sensor, motor control and RasPi With all the data transformed to digital signals, now it is possible to process the information and implement the algorithms required for the compact ventilator. However, all the information captured by the sensors must be retrieved to the RasPi and must also control the speed of the motor according to the pressure feedback. As was previously explained, the temperature sensor operates over I 2 C bus and the ADC over SPI bus. The RasPi incorporate these functions on the chip and also provides PWM for motor controlling. In the following section how these buses work and are connected to the external hardware will be explained I 2 C protocol communication for the temperature sensor The I 2 C is a two wires serial single-ended computer bus invented to connect electronic peripherals. This system was developed by Philips. It also includes a protocol to interchange data between a master and slave. The RasPi which is the master of the circuit includes in its chip 2 pins which support I 2 C. These are the GPIO 0 (Pin 3) acting as serial data line (SDA) and the GPIO 1 (Pin 5) as serial clock (SCL). The thermometer sensor which is the slave of the circuit is connected to pin2 (SDA) and pin 4 (SCL). All transfers of information take place under control of the master, as previously mentioned the RasPi as master will provide the clock signal for all 35

47 transfers. The communication takes place when the master changes the start bit followed by the 7-bit address of the thermometer IC and followed by an 8- bit which indicates weather it wants to write (0) to or read (1). The data bytes are sent serially by representing either 1 or 0 according to the bit read, starting with the most significant bit (MSB) and the slave responses with and acknowledge (ACK) signal that the data was received successfully. The master and slave continue in either receive or transmit mode according to the 8-bit sent and the slave responds accordingly. Finishing the transition is achieved by a stopping sequence where the SDA changes from low-to-high while the SCL is high. In this project the thermometer sensor was allocated by default to the hexadecimal (HEX) address 0X4D ( b). Reading a temperature value from the sensor is performed by sending the HEX command 0x00 ( b). As a result, the sensor will respond with a HEX value according to the temperature read with a sensitivity of 1 C, this means that all decimals are rounded up. In the following table there is an example of how the temperature is expressed by the sensor. Actual Temp. Registered Temp. Binary HEX C Table 10 Equivalent of temperature in HEX values The full support of I 2 C and SPI in the RasPi was achieved by using the kernel 3.2 ( r1 built on ) compiled by Chris Boot (31). However, SMBus was the only library in Python available to run the RasPi s I 2 C module. 36

48 SPI communication protocol This is another communication method between ICs. It was developed by Motorola as a synchronous serial data link that operates in full duplex mode. This operates as the I 2 C establishing a master and slave in the circuit. The main difference compared to the I 2 C bus is that SPI uses 4 wires instead of 2. It also allows full duplex mode because the input and output use different wires. The interface specifies 4 logic signals that can be identified on the RasPi and the connection to the ADC MCP3008 as follows: Term Abbreviation Pins on RasPi Pins on ADC SCLK Serial Clock GPIO 11 PIN 13 MOSI Master Output GPIO 10 MISO Master Input GPIO 9 D OUT Serial Data Out PIN 12 D IN Serial Data In PIN 11 CS Chip Select GPIO 8-7 PIN 10 Table 11 SPI Pin assignment on RasPi and ADC At the moment of the implementation of the project, there was not support for the SPI module on the RasPi. As a result, an algorithm to communicate using this protocol was required using the same pins as the SPI on the RasPi and using GPIO 25 as chip select (CS) line. This pin is the first associated with data transmission, when its state is changed to 0 by the RasPi this means that data transmission will start. Then the SPI clock is sent by the master to receive and send data to and/or from the slave. One advantage of SPI is that is not just limited to words of 8-bit that is why transmitting the 10-bit words form the ADC is more convenient with this protocol than the I 2 C.. 37

49 Volts (V) PWM pin for motor control The PWM embedded in the RasPi is one of the functions which allows the motor to run. The PWM works by giving an equivalent voltage of 0V to 3.3V when a decimal value is written on its register value. The PWM works at 100 MHz (36) which is quick enough to create any noise caused by the rotation of the motor on the audible spectrum. The PWM works with a 10-bit register. It operates by assigning a value to the PWM register equivalent to the duty cycle of the signal. For instance, if 0 is assigned to the register the duty cycle will be 0, hence the effective voltage is 0. However, if the value assigned to the register is 512 this will deliver 50% of the duty cycle which is equivalent to 1.65 V and so on. Equation 6 shows the average voltage output when a value from is assigned to the PWM register. Equation 6 Relation voltage output on PWM pin with PWM register value y = x - 6E-16 R² = PWM Register Value Figure 20 Relation between PWM register and output in Volts Figure 20 illustrates the relation between the decimal value written in RasPi PWM register and its output in volts register. Performing the linear regression 38

50 of the graph, an equation expressing the relation between the two values can be found. Equation 7 Relation PWM realtion with the output of the signal Interconnecting the PWM pin of the RasPi with the half H-Bridge was performed using the buffer explained in section 4.1 and the schematic showed in Figure 13. The minimum voltage to start to operate the motor was found when the PWM register had a value of 850. By using Equation 6 it can be found that the starting voltage to kick off the motor is 2.73 V Algorithm implementation The implementation of the algorithms was accomplished using the programming language Python 2.6. Different libraries were needed to enable the functionalities such I 2 C and in general the handling of the RasPi s expansion port. There are three different libraries available on Python to use the expansion port on the RasPi. However, there is not a library capable of handling all the IO functionalities like input/output control, I 2 C, SPI and PWM. For the present project, two libraries were used for this purpose. The library wiringpi (37) developed by Gordon Drogon which enables Python to control the input/output ports including the PWM pin. Nonetheless, this library does not support I 2 C yet. Therefore, the library SMBus was used instead which access directly the I 2 C module embedded on the RasPi. For the SPI port, as explained before there was not a library capable to handle this module, hence a program was implemented to read the data coming from the ADC circuit. In terms of general programming the following libraries were used with the aim of providing different features to the algorithms. curses library: This library is used to develop the user interface. It provides support for working with windows. 39

51 sys library: access some variables used by Python interpreter. time library: It allows to use delays on the algorithms. os library: Enable python to execute Linux commands directly to the bash. multiprocessing library: used to execute two tasks at the same time. Especially useful to print graphics while reading sensors. decimal library: allows Python working with decimal point values. Is used to make the mathematical calculations of the algorithms. xml.etree.celementtree library: The configuration files were written using XML files. This library permits Python to create, read and write XML files. SMBus library: supports the use of the I 2 C ports on the RasPi CPAP Algorithm The CPAP algorithm was designed with the goal to provide positive air pressure during the whole treatment of the patient. The first step is to set the therapeutic pressure on the XML configuration file using a scale of cmh 2 O. There are two variables required to be set before running this mode: CPAP_static_pressure and CPAP_Delay. The first variable stores the pressure of the therapy. This means the value that the device should keep during the whole treatment. The second variable CPAP_Delay is used to refresh the readings from the sensors on the screen. Finding the relation between the speed of the motor and the pressure in closed loop was performed using the methodology explained in section 5.1. Using Equation 8 the value required to be applied to the PWM register can be found to apply a set positive pressure. The algorithm flowchart of the CPAP mode describing all the methodology used can be found in Appendix E CPAP Algorithm flowchart. Figure 21 illustrates the information shown when this mode is running. The top left window shows the readings from the sensors and the window messages illustrates the PWM values set and the readings from the ADC. It also 40

52 includes a graphic gauge that displays the level of pressure from 0 to 15 cmh 2 O. Figure 21 CPAP mode window APAP Algorithm This algorithm is aimed to keep the pressure at a certain level of aperture. This means that if the level of occlusion is bigger the motor will kick off harder to try to achieve the pressure goal. However, if the occlusion level of the UA is wide, the device will try to adjust to this new pressure by adjusting the pressure of the motor. This program uses three variables loaded from the XML configuration file: APAP_Pressure which is the value of the pressure to be set, APAP_Starting_Motor where the starting value of the PWM is stored when the algorithm starts, APAP_Delay for the update time and APAP_motor_limit_speed which stores the value when the motor reaches the maximum speed and the maximum leak is occurring. Finding the values where the APAP mode should auto adjust to the pressure store in the variable APAP_Pressure was performed by constantly reading the sensors and making adjustment within a margin of ±0.1 cmh 2 O of the pressure read. If the speed of the motor was out of the limits of the variable APAP_motor_limir_speed for a certain period of time the device will alarm that there is a leak in the system. 41

53 Figure 22 APAP mode window The full algorithm flowchart explaining the operation of this mode can be found in Appendix F APAP Algorithm flowchart. The windows layout showing the information from the APAP mode is similar to the CPAP mode BiPAP Algorithm The BiPAP algorithm is based similar to the CPAP mode but this mode uses two different levels of positive pressure during the inspiratory and expiratory cycle during a period of time. The algorithm also uses the methodology and equations from section 5.1 to find the values of the inspiratory pressure and the expiratory pressure stored in the variables of the XML configuration file. There are 4 variables that are adjusted in this mode such as expiratory pressure (variable BiPAP_max_pressure), inspiratory pressure (variable BiPAP_min_pressure), inspiratory time (t i variable BiPAP_ti) and expiratory time (t e variable BiPAP_te). Assigning the values of the PWM register for minimum and maximum pressure was done by using Equation 8. The values wrote in the variables are transformed to PWM register values. Then the system is constantly reading the values of the pressure sensor to find if the device is working according to the set pressures. 42

54 Figure 23 BiPAP Mode window The flowchart of this algorithm can be found in Appendix G BiPAP Algorithm flowchart where the details of how this function works is explained in more depth User interface design with Python Using the Python library curses a windows system was created to design a simple user interface. On the main screen there are 7 options as follows. Figure 24 Main menu options available 43

55 1. Temperature graph: This mode allows the system to check the function of the temperature. It is continuously running the sensor and plotting every second the temperature is read. Figure 25 Temperature reading mode 2. Pressure graph: As well as the temperature function, this mode plots the readings from the sensor. This function continuously runs the pressure sensor and checks the function of it. It plots the decimal ADC value. Figure 26 Pressure reading mode 44

56 3. CPAP Mode: This mode executes the algorithm in section It also shows an information window with the actual reading of the pressure and the messages received from the sensor in case of any error. For an example of how this mode looks like refer to Figure 21 CPAP mode window. 4. APAP Mode: This mode executes the algorithm in section As in the CPAP Mode it shows the actual reading of the pressure and the messages received from the sensor in case of any error. It also shows a pressure bar indicating the level of pressure on a scale of 0 to 20 cmh 2 O. See Figure 22 APAP mode window. 5. BiPAP Mode: This mode runs the algorithm as per section and the screen look can be seen in Figure 23 BiPAP Mode window. This mode shows the pressure graph with a scale from 0 20 cmh 2 O. On the information window, the pressure readings are shown for expiratory and inspiratory processes. 6. Configuration: This option executes an external program to read/write the value of the configuration files written on XML. There are three files generated for each mode. Modifying these options will give a new operation mode of the device. Figure 27 Configuration window 45

57 5. Results The results obtained from the device were quite impressive considering that the compressor used was a vacuum cleaner. The following results were obtained by using calibrated instruments from King s College Hospital. Different types of equipment were required to achieve these results including pressure meters and flow meters Relation speed of the motor pressure in close loop Finding the optimum motor operation was required as an integral part of the project, especially the relationship between the speed of the motor and the positive pressure that it exerts on the UI. To find the relation between these two variables an experiment was performed. It consisted of a program on Python were values were written in the PWM register with the aim of increasing the revolutions of the motor. Then using the external pressure meter BP-SiM (Rigel Medical, Seaward Group Company, USA) shown in Figure 28, the value read was typed in using the keyboard when requested by the program. During this experiment it was found, that the point when the motor starts is when the value on the PWM register was 850 (2.73 V from PWM pin). Figure 28 Pressure meter RIGEL BP- SiM 46

58 ADC Value (Decimal) In this order of ideas, a program was written where a loop assigned values to the PWM register from 800 to 1020 on increasing steps of 10. In every step, the program asked to input the value read from the calibrated external pressure meter. The results of this experiment are expressed as a full table of data were the relation between the PWM register and the pressure was found -see Figure 29. To find the mathematical relation between the speed of the motor and the PWM register, the polynomial regression of 4 th order of the graph was performed getting a formula showing the relation between these two variables. These equations were used when required to adjust the compact ventilator to a fixed pressure such as in the case of the CPAP (See section 4.5.1) and BiPAP (See section 4.5.3) mode. For higher accuracy 8 decimal places points were used as shown in Equation y = -4.85E-06x E-02x E+01x E+04x E+06 R² = 9.96E Motor step (Decimal) Motor step vs Pressure sensor ADC value Poly. (Motor step vs Pressure sensor ADC value) Figure 29 Relation PWM register versus converted values from pressure sensor Equation 8 Relation between PWM register and pressure sensor This equation represents the underpinning fabric of the project because it transforms the speed of the motor into pressure by just assigning a decimal value to the PWM register. 47

59 mmhg 5.2. Calibration curve of the device Using the program and methodology described previously in section 5.1 the calibration curve of the pressure sensor was obtained. All the data collected from the ADC values are read from the sensor and comparing them to the readings from the external references one is able to obtain the calibration line of the compact ventilator. By performing a linear regression on the calibration line it is possible to obtain the equation that is also used in the algorithms described previously for the CPAP, APAP and BiPAP modes. This equation represents the pressure on a scale of cmh 2 O for the values coming from the ADC. As can be seen in the trend in Figure 30 the pressure sensor has a linear response as was expected. All the measures were taken in the area of interest which is the positive pressure side of the sensor, from the decimal value of the ADC at 750 which is equivalent to nearly 2.5 V. However, there are some values that are read a little bit out of range. These are external noises caused by the rotation of the motor or coupling of the external power supply. This is one of the future problems that should be revised in the future y = 8.03E-02x E+01 R² = 9.99E Sensor (DAC) Pressure reading Linear (Pressure reading) Figure 30 Calibration line using external reference Using the linear regression of Figure 30, the trend shows a real relation between the volts (or decimal value of the ADC) and the pressure read. The formula obtained from this experiment is used to express the pressure in 48

60 mmhg mmhg or cmh 2 O in the algorithms implemented. To make this application more accurate, 8 decimal values were used in the calculation of the pressure. Equation 9 Real calibration curve in cmh 2O using BP-SiM Pressure Meter Pressure sensor error margin between datasheet and calibration line Comparing the two data obtained between the expected calibration line of the pressure sensor and the calibration obtained in section 5.1 the error difference between real and expected readings can be found. As can be seen on the graph, there is a strong correlation between the two values. In conclusion, it can be seen that the pressure sensor is calibrated and its linearity is kept by the circuit. However, there is an offset level on signal acquired. These possible offset could be caused by non-linearity on the power supplies or parasite capacitances on the circuit. However, the accuracy of the compact ventilator is ensured with the new calibration curve PWM (Register Decimal Value) Reading from external pressure meter Expected read from pressure sensor Figure 31 Graph real calibration versus expected calibration Performing the correlation analysis between the values read from the sensor and the expected values a strong correlation of 0.99 was found. This means that the reliability of the new calibration line is quite high. This value was 49

61 found using the Data Analysis Tool add-in from Microsoft Excel. The following table shows the results obtained from that tool. Pressure read (mmhg) Expected pressure (mmhg) Pressure read (mmhg) 1 Expected pressure (mmhg) Table 12 Correlation calibration line versus pressure sensor expected values 5.3. Range of operation of the device In section the expected range of operation of the pressure sensor is from 0 20 mmhg. However, this is counting on the compressor being capable of generating this pressure on closed loop. During the experiments with the device some limitations to the motor were found. One of them was the lack of capacity to reach the maximum pressure of commercial medical devices at full speed, as expected. However, for practical information the maximum pressure reached for the compressor was enough to perform a good analysis of the motor and the functionality of it. The main characteristics of the compact compressor will now be described Air flow range There was not a way to know the maximum air flow of the compact ventilator without performing and experiment because there were no technical specifications attached to the device. The maximum flow of the compressor can only be determined by using an external instrument. In the workshop of the medical equipment management at King s College Hospital a flow meter shown in Figure 32 model Certifier FA Plus (TSI Incorporated, USA) was used to find the flow range of the designed compact ventilator. As part of the experiment a corrugated hose of 1 m length was used to recreate the testing environment for a compact ventilator. At the end of this hose, the measurement instrument was connected. 50

62 L/min Figure 32 Flow meter Certifier FA Plus An algorithm in Python similar to the pressure experiment done in section 5.1 was produced. The algorithm runs the motor writing values in the PWM register from 850 until 1020 in steps of 10 and the value read from the flow meter instrument is asked to be input. Then a coma-separated values (CSV) file was generated were the dynamic characteristics of the device was obtained. As can be seen from Figure 33 the operation range of the compressor is from litres per minute (L/min) at maximum speed. These values were completely outstanding coming from a portable vacuum cleaner. Probably, the flow could have been increased even more if leaks around the case were sealed. However, this information is quite valuable for the design of a future compressor as a linear function can be seen from the trend line of the device y = x R² = PWM Register Value Flow readings Linear (Flow readings) Figure 33 Flow graph versus speed of the motor 51

63 Maximum pressure The maximum pressure of the device was also found by applying the maximum value of 1023 on the PWM register. With this value on the register, it is possible to ensure an output voltage of 3.3V on the PWM pin is achieved as per Equation 6. As a result, the maximum pressure is found using the external pressure meter BP-SiM (See Figure 28) as reference for reading the pressure output. Consequently, the maximum value achieved by the compressor was 8.53 mmhg or 11.6 cmh 2 O which is enough for study purposes but not enough for therapeutic pressure Compressor noise levels One of the main characteristics of modern ventilators is the ability to run quietly. Most of the devices run in the order of db. However, these devices have been designed including sound cancelling foam and enclosures that reduce the level of noise emitted by their motors. When including a commercial vacuum into the project it was expected that the design of the device was not sound proof. It is really noisy compared with other devices. To measure the levels of noise an application from iphone (Apple Inc, Cupertino, California, USA) capable of measuring levels of noise was used. The application selected is called Decibles 9. Using this application it was found that the level of noise emitted from the device from a distance of 1 meter is 65 db which is considerably noisy compared to medical devices which run at less than 30 db Running temperature The device was tested to check were the hot points of the circuit or the compressor were. The compact ventilator was run at full speed for 5 minutes with the aim to check the running temperature of the device. Measurements 9 The application link on itunes is by David Bannach, released 29 July

64 using a calibrated thermometer Kane-May KM330 as in Figure 34 (Comark Limited, Hertfordshire, England) from King s College Hospital were taken. Figure 34 Thermometer Kane-May KM330 It was found that the compressor runs at room temperature because its motor is cooled down by the incoming air. The Raspberry Pi runs relatively cool at 35 C. This is because at the moment there is no cover on the device and Linux is not runing heavy programs but it is expected that this device will run hotter if was working closer together with the other circuits. However, the warmest part of the device was found on the H-Bridge s transistor. As it was expected these components were driving a lot of current hence making the circuit warm. The peak temperature was found at 57 C. This could be improved by designing better H-Bridge components and using a better type of motor Power supplies The device requires the use of two power supplies because the Raspberry Pi uses a mini-usb connector type as power supply and this just can reach 5 V and around 750 ma. On the other hand, a second power supply that delivers 12 VDC and 3.5 A was used to power up the compressor. In total, the whole system consumes 4.25 A when running at full capacity. 53

65 A complementary circuit to get 5 VDC from the 12 VDC power source could be designed. However, for this stage of the project it could be left as being a suggestion to be implemented in the future. The energy consumption is equivalent to a modern ventilator device. However, this consumption is still too high for the device running on batteries. This is one of the points that could be improved for future designs including a compressor that consumes less energy. 54

66 6. Discussion of results The results obtained were quite outstanding from the point of functionality with the materials used. The first objective of building a compact ventilator capable of running the three modes of CPAP, APAP and BiPAP was met. The device can also run within the specifications of medical equipment. For instance the standard pressure characteristic of a NIV for the treatment of OSA is from 0 20 cmh 2 O, as it was found on section the operation range of the pressure sensor was cmh 2 O. The calibration of the pressure sensor with the pressure meter BP-SiM (Figure 28) brought more accuracy to the system. The measurement of the new pressures obtained using the new calibration line were more close to reality as it was shown in the experiment when the accuracy of the system was verified. There are some issues with oscillations on the pressure readings when the motor is running at high speed. This noise could be created by back electromotive force generated by the brushed motor. This means that it is not possible to read a steady value and it generates inaccuracy in the readings. However, this issue tried to be mitigated by taking the average value of every 5 readings. The device created responded within the expected operational range of the pressure sensor. It is capable of delivering from 0 up to 11.6 cmh 2 O of positive pressure from a portable vacuum cleaner, which is quite unexpected and helpful for the analysis of the device. However, it is expected that the future compressor designed by Dr. Keith Pullen could be capable to reach up to 20 cmh 2 O required for this type of compact ventilators. Another dynamic characteristic found in the compact ventilator is the flow range. This device produces a maximum flow of L/min which is capable of generating a maximum pressure of 11.6 cmh 2 O as mentioned previously. Performing an analysis between the pressure and the flow a scatter diagram was produced as seen in Figure 35. This figure represents the dynamic response of the compressor. Regression analysis was used to predict the response of the compressor to generate high pressures. By 55

67 cmh20 forecasting 35 periods of the graph the maximum flow to produce 20 cmh 2 O can be found at 145 L/min. This information found is quite significant for the design of the future compressor. It should be able to meet the characteristics previously described y = x x L/min Relation Flow vs Pressure Poly. (Relation Flow vs Pressure) Figure 35 Relation flow versus pressure 6.1. Testing the compact ventilator A test to find the accuracy of the compact ventilator was performed. This test was done as per the performance check done on a medical ventilator CPAP or APAP at King s College Hospital as seen in Figure 36. It consisted of a 1 m long standard PVC corrugated hose attached to the device and a dummy lung with 2 L of capacity at the end of it. Between the dummy lung and the hose a T joint was placed with an output hose where the pressure meter was connected. As a result, the pressure measured at the end of the hose is the one that the patient will receive. Because this is a closed system loop, it is expected that the pressure through the whole hose is the same. This means that the pressure from where the sensor is taking the sample should be the same at the end of the hose. The results obtained using this methodology was very accurate and within a low error margin. The device was run on CPAP and APAP mode on 5 different levels of pressure and the result was compared with the set value, 56

68 the read value from the sensor and the read value from the external reference. Figure 36 Setting for testing the compact ventilator's accuracy A test in BiPAP mode could not be performed for 2 reasons, first the calculations to find the pressure on the CPAP mode are used in BiPAP mode, hence it is expected to have the same results on the readings. Secondly, the response of the pressure meter BP- SiM was not quick enough to capture the readings before the change from inspiratory time to expiratory time. CPAP Set (cmh 2 O) Sensor Read (cmh 2 O) Rigel Read (cmh 2 O) Error (%) CPAP vs Sensor Error (%) CPAP Vs Rigel Error (%) Sensor Vs Rigel % 10.0% 0.0% % 0.0% 2.0% % -2.9% -1.5% % 0.0% 1.1% % 3.0% -1.0% Average: 2.2% 2.0% 0.1% Correlation: Table 13 Test results in CPAP mode 57

69 cmh2o Table 13 shows the results obtained from testing the compact ventilator in CPAP mode with 5 different pressures set. As can be appreciated, the compact ventilator is very accurate. However, the only pressure with a great variance was when the device was running with settings at 3 cmh 2 O and 7 cmh 2 O. However, it can be seen that the accuracy between the value read from the sensor in the device and the external pressure meter were very similar with an average error of 0.1% and a correlation of The strong correlation between the value set and the readings from the sensor and the external pressure meter (0.997 in both cases) can also be appreciated. This indicates the high reliability of the results obtained. Nonetheless, it can be seen that the configuration line on the low values may be slightly inaccurate Sample CPAP Value Set External Pressure Meter (cmh2o) Sensor Reading (cmh2o) Figure 37 Error comparison in CPAP mode This is confirmed by Figure 37 were a comparison of the 3 errors is plotted. By selecting a margin error of 5% in the plot, the only value out of this range is the 3 cmh 2 O but the rest are within the specifications. However, this result is quite impressive considering that in the medical field the error is not given in percentage but in a margin of roughly ±0.5 cmh 2 O of the value set. If this consideration were used in this device, it could be said that this compact ventilator will pass the reliability test for a CPAP device. 58

70 The APAP test results are even more accurate. It seems that the algorithm used to adjust the pressure within the set value increases the accuracy and reliability of the device. As can be seen in Table 14 the average error between the pressure set, the sensor reading and the external pressure meter was less than 0.5%. This indicates that by using this technique, the pressure delivered by the compact ventilator is highly accurate. This can be confirmed by the correlation calculated between the readings, as shown by the table this correlation is The only big difference was found in the low pressure setting, as in the CPAP mode were the pressure read by the BP-SiM device was as the value set, but the value read by the sensor was 0.1 cmh 2 O below the pressure set. However, this value is not very significative and as well as the CPAP mode, this mode will also pass the reliability test in a clinical environment. APAP Set (cmh 2 O) Sensor Read (cmh 2 O) Rigel Read (cmh 2 O) Error (%) APAP vs Sensor Error (%) APAP Vs Rigel Error (%) Sensor Vs Rigel % 0.0% -3.3% % 0.0% 2.0% % -2.9% 1.5% % 2.2% -1.1% % 1.0% -1.0% Average -0.3% 0.1% -0.4% Correlation Table 14 Test results in APAP mode Plotting the 3 errors and selecting to graph a 5% margin error within the set value of the APAP pressure as in Figure 38, it can be appreciated that the APAP mode is within the range for every pressure. This confirms also the accuracy of the device which is quite impressive considering the characteristics of the compressor. However, the algorithm 59

71 cmh2o implemented, incremented the accuracy of the compact ventilator quite notably. This confirms once again the reliability of the compact ventilator designed. The little discrepancy of the pressure value on the low settings could be solved by performing a new calibration Sample APAP pressure set Sensor Reading (cmh2o) External Pressure Meter (cmh2o) Figure 38 Figure Error comparison in APAP mode 6.2. Raspberry Pi as a tool for projects in clinical engineering It was demonstrated that using a Raspberry Pi as an inexpensive tool to create projects in the clinical engineering field is viable and cost-effective. The low cost of the device and the capability of using high level programming language makes it an interesting candidate to create a new generation of projects. The use of the RasPi requires some previous knowledge of the Linux world as well as programming to get the most out of it. However, one of the disadvantages of using the device is that there is not much published information available. This is completely understandable because it is a relatively new product but it is expected that the interest of the academic community in this product will increase the amount of applications and libraries to run all the modules included in the RasPi. 60

72 7. Conclusion This dissertation project fulfils the main objective, of creating a compact ventilator capable of delivering three operation modes CPAP, APAP and BiPAP. The model created is capable of running compressors working at 12 VDC and is able to measure the positive pressure generated by the device with an extraordinary capability and accuracy. It was very impressive that using a common portable vacuum cleaner as a compressor was able to deliver a good range of operational positive pressure with air flow of up to L/min and capable of generating 11.6 cmh 2 O. This fits within the operational range of other commercial ventilators which run from 0 20 cmh 2 O but is still limited by the architecture of the compressor and because its main purpose is not to blow air but to suck air. The device demonstrates that it is able to run in CPAP, APAP and BiPAP mode by delivering positive pressure within a previously set value. The error margin found was around 2% for the CPAP which is completely acceptable within the CPAP market. The APAP mode worked even better than the CPAP mode. The margin error was 0.5% which is extremely accurate for the device and it is completely acceptable within a clinical measurement. However, the algorithm used needs to be improved in a way that automatically finds the occlusion pressure and level the device within this level of pressure. It was also found that the Raspberry Pi could be an inexpensive tool to be used in the clinical engineering area. The only additional components needed are ADCs and buffers, thus a full low cost data acquisition system with sensors included could be implemented. This project has generated a basic platform to develop a further project were the compressor of Dr. Keith Pullen could be tested and verify if it could potentially be more effective than modern compact ventilators. If some improvements are performed on the motor, this could be a fully portable device that could be a breakthrough in the industry. 61

73 8. Suggestions for further work Promising results were obtained from this dissertation project. However to create a fully functional medical device some suggestions should be taken into count. The first suggestion is to create a fully portable device. As it was shown, the RasPi does not consume too much current which makes it a good candidate as a portable device. The motor compressor would consume most of the energy and this must be taken into consideration when designing the final product. In this case, a new compressor from Dr. Pullen s design should be created considering a different type of motor. It would be recommended that this new compressor use a low consumption energy electrical motor. The best option could be a brushless-motor with low voltage and current. Minimising the consumption of the motor, will ensure a minimum size on the device and ensure its portability. The second suggestion is to improve the operation of the compressor. If the compressor were capable of generating high air flow by lower revolutions this could also considerably reduce the energy consumption of the device. The minimum recommendation for the compressor for being used in a NIV device is to generate L/Min of airflow capable to generate 0 20 cmh 2 O of pressure using a standard output of 22 mm of diameter. If a small output is going to be used, new calculations should be performed. A third suggestion in the design of the compressor is that the motor could include an embedded tachometer. Knowing the real speed of the motor will allow creating better control algorithms using the real revolutions per minute of the motor and increasing the accuracy of the device. From the circuit point of view there are other issues to be improved. The H- Bridge runs too hot at 57 C. However, if the first suggestion is taken into account, this will minimize the load on the transistors. Hence, the heat generation will decrease and the device will run cooler. Another improvement that could be done on the circuit is the elimination of the noise read from the sensors. One problem during the implementation of 62

74 the device was that when the motor was running the values read from the sensors started to ripple and change values randomly. This noise could possibly be caused by noise created by the brushes of the motor-compressor used and the quality of the power supply used. This could also be improved by using a more sophisticated motor and better power supplies. There are some improvements to be performed from a software point of view. Minimising the use of libraries, when the new versions of the libraries come around this would assist in supporting all the modules on the RasPi. This means that just one library will be enough to run the I 2 C, SPI and PWM, improving the quality of the compact ventilator. The multiprocessing can also be improved, in the present algorithm the competency to continuously read the sensors while showing results is limited. This means, that real time measures cannot be taken yet. However for this project real time is not a big necessity but for other type of projects it may well be necessary. Additionally, as previously explained in the conclusions, the APAP algorithm could also be improved by making it smarter and find the level of occlusion without setting a desired value. Lastly, the user interface can be improved by using extra hardware and software. At the moment, the device is just run by using commands introduced by the keyboard. Nonetheless, this could be improved with external hardware such a selection knob or keypad and a liquid crystal display (LCD). The environment running from the RasPi can be improved by creating a friendlier user interface. The library curses was used to create a basic interface, however a fully graphic environment can be created using the desktop version of xserve. 63

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77 Appendix A ResMed S9 Series Specifications MODES S9 ESCAPE S9 ELITE S9 AUTOSET CPAP AutoSet CORE HARDWARE FEATURES Dimensions (L x W x H) 153 mm x 140 mm x 86 mm 153 mm x 140 mm x 86 mm 153 mm x 140 mm x 86 mm Weight 810 g 810 g 810 g Sound levels 24 dba (2002 standard)* 26 dba (2007 standard)** 24 dba (2002 standard)* 26 dba (2007 standard)** 24 dba (2002 standard)* 26 dba (2007 standard)** Pressure range (4-20 cm H2O) EPR (OFF-3cm) Ramp 0FF-45mins Automatic altitude adjustment Automatic leakage compensation International AC input ( V) DC input Carry bag DATA Mask Fit SmartStart Sleep Quality Indicator Usage only Recurring reminders Compliance logging/mask on time: - AHI - Pressure - Leak - Central Apnea Index (CAI) High-res flow (25Hz) (via SD card) (via SD card) DATA OPTIONS SD card S9 Oximeter adapter TUBING SlimLine tube ClimateLine tube Optional Optional Optional Standard tubing Optional Optional Optional SOFTWARE COMPATIBILITY ResScan 3.11 HUMIDIFICATION Integration with heated humidifier H5i H5i H5i Climate Control Optional Optional Optional w/heated tube w/heated tube w/heated tube A-1

78 Appendix B CPAP market comparison Basic Features Transcend II Travel PR Plus CPAP S9 Elite Manufacturer Somnetics Philips Respironics ResMed Type CPAP CPAP CPAP Warranty 2 Years 2 Years 2 Years Sound Level 29 dba 29 dba 24 dba Quiet Yes Yes Yes Manuals Included Patient/Quick Setup Patient Patient/Quick Setup Dimensions Travel Size Yes Yes Yes Under 3 lbs Yes Yes Yes Machine Weight 0.94 lbs 2.2 lbs 1.8 lbs Entire Weight 2.4 lbs 6.5 lbs 7.4 lbs Entire Size 6.1" x 3.5" x 2.8" 6.5" x 10.75" x 4" 11.25"x6.25"x3.25" Entire Package Size 9" x 4" x 10" 8 " x 14.75" x 8.5" 14.5"x 12.5"x5" Machine Size Only NA 7" x 5.5" x 4" 6" x 5.5" x 3.25" Humidifier Features Heated Humidifier HME Waterless Technology B-1 System One & Dry Box Built-In Humidifier No No No Heated Humidifier Type Integrated Integrated Integrated RainOut Reduction HME Waterless Tech. Humidity Control Climate Control Integrated Heated Hose No No Yes Pressure Features Auto Pressure Adjustment No No No Pressure Range 4-20 cmh2o 4-20 cmh2o 4-20 cmh2o EPAP Pressure Range NA NA NA Easy Breathing No C-Flex EPR with Easy-Breathe Ramp Yes Yes Yes CPAP Mode Yes Yes Yes Power Features Direct Battery Operation Yes Yes Yes Voltage Range V AC V AC V AC Optional DC Cable Yes Yes Yes Integrated Battery No No No Software Features Advanced Software/Data OS No MSD; OS Data Card No Yes Yes Optional Software Yes Encore Viewer 2.0 ResScan Additional Features H5i

79 Auto Altitude Adjustment Auto Manual Auto Auto ON/OFF No Yes Yes Mask OFF Alert No LCD/Alarm No Spontaneous No No No Timed No No No Designed For Her No No No *Note: This table has been done using the comparative tool of the website B-2

80 Appendix C APAP market comparison S9 Elite PR Plus CPAP Basic Features Manufacturer ResMed Philips Respironics Type APAP APAP Warranty 2 Years 2 Years Sound Level 24 dba 29 dba Quiet Yes Yes Manuals Included Patient/Quick Setup Patient Dimensions Travel Size Yes Yes Under 3 lbs Yes Yes Machine Weight 1.8 lbs 2.2 lbs Entire Weight 7.4 lbs 6.5 lbs Entire Size 11.25"x6.25"x3.25" 6.5" x 10.75" x 4" Entire Package Size 14.5"x 12.5"x5" 8 " x 14.75" x 8.5" Machine Size Only 6" x 5.5" x 3.25" 7" x 5.5" x 4" Humidifier Features Heated Humidifier H5i System One & Dry Box Built-In Humidifier No No Heated Humidifier Type Integrated Integrated RainOut Reduction Climate Control Humidity Control Integrated Heated Hose Yes No Pressure Features Auto Pressure Adjustment No No Pressure Range 4-20 cmh2o 4-20 cmh2o EPAP Pressure Range NA NA Easy Breathing EPR with Easy-Breathe C-Flex Ramp Yes Yes CPAP Mode Yes Yes Power Features Direct Battery Operation Yes Yes Voltage Range V AC V AC Optional DC Cable Yes Yes Integrated Battery No No Software Features Advanced Software/Data MSD; OS MSD; OS Data Card Yes Yes Optional Software ResScan Encore Viewer 2.0 Additional Features Auto Altitude Adjustment Auto Manual Auto ON/OFF Yes Yes C-1

81 Mask OFF Alert No LCD/Alarm Spontaneous No No Timed No No Designed For Her No No *Note: This table has been done using the comparative tool of the website C-2

82 Appendix D Schematic design of the compact ventilator D-1

83 Appendix E CPAP Algorithm flowchart LOAD XML CPAP_Pressure CPAP_Delay CPAP Mode RUN CPAP USING VAR CPAP_Pressure CPAP_Delay READ SENSORS PRESSURE TEMP RUN I2C ROUTINE RUN SPI ROUTINE EXIT Is Q pressed? ACTIVATE MOTOR USING VAR CPAP_Pressure DISPLAY PWM actual Value PRESSURE WAIT CPAP_Delay time E-1

84 Appendix F APAP Algorithm flowchart LOAD XML APAP_Pressure APAP_Starting_Motor APAP_Delay APAP_motor_limit_speed NO NO APAP Mode DEFINE LIMTS Reach APAP Pressure Is 10% > APAP Max and min Limit >10% YES Is Q pressed? YES EXIT CALCULATE PWM Register Value Starting Press 10% > APAP Max and min Limit >10% READ PRESSURE SENSOR GET PWM Value If pressure sensor <= APAP_Pressure NO Yes INCREASE MOTOR SPEED PWM+1 If pressure sensor > APAP_Pressure YES DECREASE MOTOR SPEED PWM-1 NO Is PWM >= APAP_Motor_Speed_ Limit? Yes PWM = APAP_Motor_Speed_Limit F-1

85 Appendix G BiPAP Algorithm flowchart EXIT YES LOAD XML BiPAP_max_pressure BiPAP_min_pressure BiPAP_ti BiPAP_te NO Is Q pressed? BiPAP Mode DEFINE PARAMETERS ACTIVATE MOTOR BiPAP minimum pressure Wait BiPAP_ti (sec) ACTIVATE MOTOR BiPAP maximum pressure Wait BiPAP_te (sec) CALCULATE PWM Register value for BiPAP minimum pressure BiPAP maximum pressure READ PRESSURE SENSOR EQUATION Polynomial Relation Motor Vs Pressure Return PWM motor speed for min press PWM motor speed for max press DISPLAY PRESSURE & GRAPH G-1