Implementation of a Mobile Health System for Monitoring ECG signals

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1 Implementation of a Mobile Health System for Monitoring ECG signals Nassim Amour 1, Ahmad Hersi 2, Naif Alajlan 1, Yakoub Bazi 1, Haikel AlHichri 1 1 Advanced Lab for Intelligent Systems Research (ALISR), Department of Computer Engineering King Saud University, P.O. Box 51178, Riyadh, Saudi Arabia, {namour, ybazi, najlan, hhichri}@ksu.edu.sa 2 Faculty of Medicine, King Saud University PO Box 7805, Riyadh, Saudi Arabia, ahersi@ksu.edu.sa Abstract In this paper, we propose an ECG system for remote monitoring of multiple patients with cardiovascular diseases. The system is composed of three main units, the patient's unit, the server unit, and the monitoring unit. The patient's unit is composed of a wearable ECG miniature sensor, with Bluetooth capability, and a smart mobile phone with an internet connection. The ECG miniature sensor continuously measures the ECG signals of the heart and wirelessly transfers the data directly to a smart mobile phone of the patient via Bluetooth technology. The ECG signal is stored at the mobile device, displayed on the screen, and automatically transferred to the server unit through an internet connection. At the monitoring unit, which is a desktop application installed at the health care provider, the ECG signal is fetched from the server unit and displayed with an advanced GUI. The monitoring unit also analyses the signal to perform segmentation into heart beats and detection of Arrhythmias. The whole process including ECG signal transmission and arrhythmia detection is performed in real time for several patients in parallel. The number of patients monitored in conjunction depends on the computational power and network bandwidth available at the server unit. Experiments have been performed on simulated data, a real person data, and data from the MIT-BIH database. In all cases, the data is streamed into the system as if it is coming from a live patient. The results show the strong capability of the system in long term remote monitoring of patients in real time. Different experiments have also been performed to measure the accuracy of the heart-beat segmentation algorithm. The system obtained near perfect results. Keywords: ECG remote monitoring, Mobile phone applications, QRS detection, wireless ECG sensors. 1. Introduction The need for developing health-monitoring systems for hospitals, clinical or home settings that can provide efficient services to patients with cardiovascular diseases (CVD) is becoming increasingly important particularly for medium and big urban agglomerations. The utilization of a wired solution in the aforementioned settings encumbers the mobility of patients and discourages medium and long term monitoring. The new developments in information and communication technologies have made the remote monitoring of patients a promising alternative. To this end remote monitoring solutions have been increasingly introduced recently [1]-[10]. Lin at al. proposed a real-time monitoring system composed of a lightweight and a wireless ECG device connected to a mobile device via Bluetooth. The mobile device is equipped with a built-in automatic warning expert system for the detection of atrial fibrillation arrhythmia. Shih et al. [2] integrated a general packet radio service (GPRS), an embedded system, and RF identification (RFID) technologies to develop a new mobile ECG monitoring and alert system architecture to monitor elderly patients when alone. In [3], an 8-channel system is developed for capturing bioelectric signals, such as electrocardiogram (ECG), electroencephalogram (EEG) and electrooculogram (EOG), and transmit them through a wireless connection using the ZigBee protocol. Lee et al. [4] proposed a role-based mobile healthcare system for chronic disease patients with an integration of multiple physiological parameters including ECG. Gupta et al. [5] developed a lowcost ECG monitoring system for acquisition, storage, and processing of ECG signals using a MATLAB-based graphical user interface. The single lead ECG is sampled at a rate of 1 khz and then fed to a microcontroller-based embedded system to convert the ECG data to a RS232 formatted serial bit-stream. Sufi et al. [6] proposed a system that obviates the need to decompress ECG signals. They proposed a compression technique suitable for mobile phones by retaining key features of cardiovascular conditions in diagnosable but concealed format. These features allow doctors to estimate and calculate the heart beat rate and width of the QRS complex feature from compressed ECG. Wen et al. [7] proposed an ECG telemonitoring system that transmits abnormal heartbeats, which are identified in the patient-worn unit (Holter), in real time by using multimedia messaging service (MMS) and GPRS technology. The system also transfers all ECG signals acquired and stored in the Holter through internet. Wu et al. [8] presented a general architecture for a wearable sensor system that can be customized to an individual patient s needs. This architecture is based on embedded artificial intelligence that permits autonomous operation, sensor management and inference, and may be applied to a general purpose wearable medical diagnostics. Costa et al. proposed a telecardiology platform which is based on ubiquitous services. Their main idea is to decouple the typical centralized database of telemedicine platforms and replace it with a normalized mailbox. Capua et al. [10] designed of a smart ECG measurement system that is based on web-service-oriented architecture for heart status monitoring. In this system, a ASE 2014 ISBN:

2 mobile phone performs the recording and the analysis of ECG signals and in case of emergency it communicates the patient data to a remote station for assistance. In this paper, we propose a smart system for the remote monitoring of multiple CVD patients in parallel and in real time as shown in Fig. 2. This system consists of three main parts termed as patient unit, server unit and monitoring unit. The patient unit is composed of a wearable ECG miniature sensor, with Bluetooth capability, and a smart mobile phone with an internet connection. The ECG miniature sensor continuously measures the ECG signals of the heart and wirelessly transfers the data directly to the mobile phone. Within the mobile phone, the ECG signals can be stored, displayed and transferred to the server unit through an internet connection using either 3G or WIFI connection. The monitoring unit reads the ECG signals from the server also through an internet connection and then analyzes the signal in an automatic way for heart beat segmentation and detection of abnormalities. In particular this unit performs heart rate computation and then detection of two basic abnormalities, namely Brachycardia (heart rate < 60 beats per minute (bpm)) and Tachycardia (heart rate > 100 bpm). Unlike other systems in the literature, our system performs all the above steps simultaneously, in real time, and for several patients. The number of monitored patients depends mainly on the computational power of the monitoring unit. Several experimental results on simulated as well as real ECG datasets are reported and discussed. Our system also, gives the patient ownership of his ECG data and the freedom to choose any health provider he wants. The remaining part of the paper is organized as follows. In Section 2, a brief overview of ECG signals is presented. Next Section 3 gives a detailed description of the proposed system, including the intelligent analysis of ECG signals within the monitoring unit. Experimental results on simulated as well as real data are reported in Section 4. Finally conclusions and future developments are drawn in Section 5. placed on the skin at different sides of the heart to measure the activity of various parts of the heart muscle, using an instrumentation amplifier. Thus it constitutes, a noninvasive test used to reflect underlying heart condition by measuring the electrical activity of the heart. A typical ECG normal heartbeat (or cardiac cycle) consists of a P wave, a QRS complex and a T wave as shown in Figure. 1 [12]-[14]. The waveform and the relationship between the different sections composing it can be used to determine the heart rate as well as distinguish various cardiac arrhythmias such as Tachycardia (high cardiac rhythm), Bradycardia (slow cardiac rhythm), atrial fibrillation, and premature ventricular contraction. The developed system is composed of three main units as shown in Figure. 3. Patient unit: contains the wireless sensor held by the patient and the mobile-phone device; Server unit: receives and stores the signals sent from the patient via an internet connection (GSPRS or WIFI). This unit serves as storage of patient lists, doctor lists and ECG data. Monitoring Unit: allows the doctors (or physicians) to access patient data in real time for monitoring the patient status. 3.1 Patient Unit 2. ECG overview (a) (b) Figure 2. (a) Shimmer ECG sensor and (b) ECG leads. Figure 1. a) Example of a normal ECG signal and b) its approximation The ECG signal is a plot of the electrical potential between various points of the body near the heart [12]. Electrodes are The patient unit is composed of a wireless ECG sensor (shown in Figure. 2) and a smart mobile phone. The wireless ECG sensor measures the ECG signals from the patient's body and transmits it to the smart phone via Bluetooth technology. In our implementation, we use a wireless 3-lead ECG sensor from Shimmer real time sensing [11]. This miniature sensor can record and transmit ECG data (lead II and lead III) in real time (see Figure. 3). Designed as a wearable ECG sensor, ASE 2014 ISBN:

3 Shimmer records the pathway of electrical impulses through the heart muscle. The recording can be done on resting and ambulatory subjects. Providing quick recovery from movement artifacts, the Shimmer ECG sensor offers a cost effective solution that captures signals to the highest industry standards. Shimmer uses the Bluetooth interface to communicate with other devices by sending a specific packet format. The main role of the smart mobile phone is to store the incoming ECG signal, display it, and retransmit it to a server over the internet. To this end, we have developed an interactive application that can run on many mobile phones running the android platform. This application allows displaying the ECG signals on screen, saving them on an external memory, and sending them to a distant server. These features can be executed in parallel or separately depending on user preferences. During the initialization phase, the mobile starts by searching for the shimmer device using its Bluetooth connection. After finding a device, the mobile opens a connection and sends a request for receiving ECG data. It is worth noting that the application can handle errors such as the loss of connections with the sensor or the distant server or both. Figure. 4 shows some snapshots of the android application implemented on a Samsung Galaxy S2 Patient Unit Server Unit Monitoring Unit Figure 3. Overview the mobile health system for the remote ECG monitroing Figure 4. Android mobile application developed on a Samsung Galaxy II Smartphone. ASE 2014 ISBN:

4 3.2 Server Unit This unit provides a central storage location for all ECG data from all patients using the system. The server side application is implemented using PHP/MySQL tools. It houses a relational database which contains all information about doctors, patients, and the ECG data. 3.3 Monitoring Unit This unit is basically a MS Windows-based application that resides at the health care specialist desktop. It allows him to monitor the heart activity of many patients in real time or offline modes. By offline mode we mean observing and shown in Figure. 5. Another important task performed at the monitoring unit, is the analysis of the ECG signal, which includes counting the heart beat rate, and detecting any arrhythmias. The current version of the system implements QRS peaks detection to heart beat rate counting, and detecting of simple arrhythmias, namely Brachycardia and tachycardia. The QRS detection problem has been investigated by several authors [12]-[14]. In this work, we implement a modified version of the QRS detection algorithm proposed by Pan and Hopkins [12]. This Algorithm is based on many steps. A descriptive flowchart of the QRS detection algorithm is presented in Figure. 6. Figure 6. QRS Complex detection algorithm (a) (b) Figure 5. Monitoring unit: (a) screen 1: multiple patients in real time and (b) screen 2: historial data and analysis details of one patient. Step 1: The signal is filtered using a band-pass filter, which reduces the influence of muscle noise, 50 Hz interference, baseline wander, and T-wave interference. The desired passband to maximize the QRS energy is approximately 5-15 Hz. To this end, we cascaded a low-pass filter and a high-pass filter to achieve a 3dB pass band approximately on the desired frequency band. Step 2: The signal is differentiated to provide QRScomplex slope information. After that the resulted signal is squared point by point to make all data points positive. Step 3: In order to obtain waveform feature information in addition to the slope of the R wave a moving-window integrator is executed and the QRS complex will correspond to the rising edge of the integration waveform. The time duration of the rising edge is equal to the width of the QRS complex. A fiducial mark for the temporal location of the QRS complex can be determined from this rising edge according to the desired waveform feature to be marked such us the maximal slope or the peak of the R wave. Step 4: Finally in the last step, an adaptive threshold is used to isolate the R peak. This threshold is computed by estimating noise and signal peak-heights. analyzing historical data where time constraint is not crucial. For efficient control and visualization, we developed an application that shows the results on two screens. The first screen is used for simultaneously visualizing ECGs of multiple patients. The second screen is used to provide more details about any patient selected by the user in the first screen as To assess the performances of the proposed monitoring system, simulated as well as real ECG data are used in the experiments. It is important to mention that all experiments ASE 2014 ISBN:

5 presented in this work are done in a real time including ECG transmission, and QRS detection. 4.1 Experiments on simulated data In this experiment, we use a simulated ECG signal obtained from the TechPatient cardio instrument shown in Fig. 7. This instrument can generate 12 leads cardiac signals and simulate many abnormalities. In addition it can also generate various forms of noise commonly available in ECG signals. We connect this instrument to the ECG sensor using 3-lead connection as shown in Figure. 7. Then we configure it to generate an ECG signal sampled at 200 Hz with different heart rates and contaminated with a Gaussian noise with zero mean and unit standard deviation. An ECG signal of 90 minutes duration is collected, containing two abnormality events which are Brachycardia and Tachycardia in addition to normal ECG events. The total number of heart beats for this signal is Figure 8. Detection results obtained for the simulated ECG signal. TABLE I. shows the quantitative results obtained for the QRS detection in the simulated data (row 1). The results obtained are near perfect as the sensitivity is 99.99% and the positive predictive is 100%. In fact, in this experiment, only the first QRS is missed due to real time execution. 4.2 Experiments on a real subject Figure 7. Simulated ECG signals generated from techpatient insturment. Signals are sent to the mobile phone by Shimmer via Bluetwtoh connection. Figure. 8 shows the detection results for the different events obtained by the monitoring unit (desktop computer with 2.4 GHz Quad core CPU and 24 GB RAM). In this experiment, the system is used by a healthy person for about 96 minutes. Figure. 9 shows and example of the received ECG which is composed of 6197 heart beats. At the beginning, the signals are acquired under normal activity for about 93 minutes. Then, for the remaining 3 minutes, the signals are acquired under a sport activity. For this scenario (see TABLE I. ), 3174 beats are correctly detected, whereas 23 beats are wrongly classified as QRS and one beat was missed. Here we point that most of the false beats occurred in the second phase under the sport activity. Here again the first QRS is not detected. Figure 9. Detection results obtained for the real ECG signal. 4.3 Experiments using the MIT-BIH database ASE 2014 ISBN:

6 In order to further asses the overall functionality of the remote monitoring system, we use ECG records from the well known MIT-BIH arrhythmia database [39]. We particularly, select the following records: 100, 102, 104, 105, 106, 107, 118, 119, 200, 201, 202, 203, 205, 208, 209, 212, 213, 214, 215 and 217. We resample the signal of these records (Lead II) from 360Hz to 200Hz and save them in the mobile phone memory. Then the mobile application initiates a real time transmission process via internet connection to the server. The monitoring unit reads these data in real time from the server and runs the QRS detection. As the system is designed for the simultaneous monitoring of many patients, we transmit 4 records simultaneously using four different mobile phones. The QRS detection results reported in TABLE II. show that among heart beats are correctly detected, 358 are missed, and 36 are wrongly detected. This overall S and P are equal to 99.31% and 99.93%. From this table, we observe that weak results are obtained for the record 203 with 147 missed heartbeats. On the other hand, the record 105 presents the worst positive predictive value. For all datasets, the global positive predictive and the sensitivity metrics are high and encouraging to perform the future work which includes detection of more advanced ECG Arrhythmia. 5 In this paper, we proposed a novel smart ECG system for the remote and real time monitoring of CVD patients. This system consists of a wearable ECG miniature sensor, which continuously measures the heart activity and wirelessly transfers the data directly to the mobile phone of the patient via Bluetooth connection. This mobile then transfers these data to a remote server using internet connection where they will be stored in a database. After that, an application on the monitoring unit analyzes the ECG signal with advanced pattern recognition methods. We implemented fast techniques to detect and segment each heartbeat in real time. The system was tested using first an ECG simulator, then a real person. The monitoring system was showing the ECG signal in real time with information about the heartbeat rate and giving alarms in case of Brachycardia and Tachycardia arrhythmia. For advanced testing, we have used the MIT-BIH ECG database of real patients. Some record of this database are saved on the mobile and sent to the server. The obtained results are very encouraging in terms of the function of the whole system and also in terms of the obtained accuracies of QRS detection. For future works, we plan to implement some light operations like QRS detection, bradycardia and tachycardia arrhythmia on the mobile phone instead of the monitoring unit. In addition, we will continue to implement arrhythmia detection on the monitoring unit using advanced pattern recognition techniques from our own research in the area. We also envisage to use the compressive sensing theory to implement a compression algorithm inside either the mobile phone or the shimmer device which will help save storage space, bandwidth, transmission time, and expand battery life of the ECG sensor. TABLE I. QRS DETECTION RESULTS OBTAINED FOR SIMULATED AND A REAL ECG CG Signal Duration # QRS TP FN FP S % P % Simulated 90 min Real Subject 96 min TABLE II. QRS DETECTION RESULTS FOR MIT-BIH RECORDS Record # QRS TP FP FN S % P % Overall Acknowledgment This work is supported by the National Plan for Sciences and Technology (NPST), King Saud University under project number 11-MED References [1] C. T. Lin, K. C. Chang, C. L. Lin, C. C. Chiang, S. W. Lu, S. S. Chang, B. S. Lin, H. Y. Liang, R. Chen, Y. T. Lee, L. W. Ko, An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation, IEEE Transactions on Information Technology in Biomedicine 14 (3) (2010) [2] D.-H. Shih, H.-S. Chiang, B. Lin, S.-B. Lin, An embedded mobile ECG reasoning system for elderly patients, IEEE Transactions on Information Technology in Biomedicine 14 (3) (2010) [3] L. Boquete, J. M. Ascariz, J. Cantos, R. Barea, J. M. Miguel, S. Ortega, N. Peixoto, A portable wireless biometric multi-channel system, Measurement 45 (6) (2012) [4] R.-G. Lee, K.-C. Chen, C.-C. Hsiao, C.-L Tseng, A mobile care system with alert mechanism, IEEE Transactions on Information Technology in Biomedicine 11 (5) (2007) [5] R. Gupta, J.N. Bera, M. Mitra, Development of an embedded system and MATLAB-based GUI for online acquisition and analysis of ECG signal, Measurement 43 (9) (2010) [6] F. Sufi, Q. Fang, I. Khalil, S.S. Mahmoud, Novel methods of faster cardiovascular diagnosis in wireless telecardiology, IEEE Journal on Selected Areas in Communication 27 (4) (2009) ASE 2014 ISBN:

7 [7] C. Wen, M.-F. Yeh, K.-C. Chang and R.-G. Lee, Real-time ECG telemonitoring system design with mobile phone platform, Measurement 41 (4) (2010) [8] W.H. Wu, A.A. Bui, M.A. Batalin, L.K. Au, J.D. Binney and W.J. Kaiser, MEDIC: medical embedded device for individualized care, Artificial Intelligence in Medicine 42 (2) (2010) [9] C. Costa, J.L. Oliveira, Telecardiology through ubiquitous Internet services, International Journal of Medical Informatics 81 (9) (2012) [10] C. D. Capua, A. Meduri, R. Morello, A smart ECG measurement system based on Web-Service-Oriented architecture for telemedicine applications, IEEE Transactions on Instrumentation and Measurement 59 (10) (2010) [11] Shimmer Platform: accessed April 2014 [12] J. Pan, W.J. Tompkins, A real time QRS detection, IEEE Transactions on biomedical Engineering 32 (3) (1985) [13] J. Darington, Towards real time QRS detection: A fast method using minima pre-processing, Biomedical Signal Processing and Control 1 (2006) [14] H. Zhu, J. Dong, An R-peak detection method based on peaks of Shannon energy envelop, Biomedical Signal Processing and Control 8 (2013) ASE 2014 ISBN:

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