Oxygen Saturation Measurement and Optimal Accuracy in Nair

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1 The Applcaton of Threshold De-nosng n Moble Oxygen Saturaton Montorng Software Tang Nng and Xu Zhenzhen School of Computer Scence and Technology, Donghua Unversty, Shangha, Chna tnwysyd@126.com ABSTRACT Oxygen saturaton s an mportant parameter of human body. It needs to be montored on many occasons. In ths thess, based on theores of reflectve pulse oxygen saturaton of non-nvasve montorng, the wrter uses the knowledge of mage processng, desgned a smart moble oxygen saturaton montorng software, and lays an emphass on how to select proper wavelet threshold de-nosng algorthm to be appled nto oxygen saturaton montorng software, and to evaluate ts accuracy. The expermental results show that the Coflet wavelet de-nosng algorthm can mprove the accuracy of oxygen saturaton measurement. KEYWORDS Oxygen saturaton; mage processng; wavelet denosng; threshold de-nosng; smart phone applcaton 1 INTRODUCTION Abundant oxygen s the elemental materal of all the lfe, and oxygen saturaton s an mportant parameter to reflect the oxygen n blood. Oxygen saturaton refers to the percentage between the maxmum amount of oxygen combned wth the hemoglobn n blood and the actual amount. In general, measurng blood oxygen saturaton can estmate the oxygen level n the blood of the human body n clncal dagnoss. Currently, the wdespread method of montorng oxygen saturaton s based on the pulse of the nonnvasve montorng of blood oxygen saturaton (SpO2) measurement technology. Meanwhle, SpO2 measurement technque s regarded as a method of nonnvasve, contnuous, real-tme montorng of arteral oxygen saturaton. In terms of the dfferent methods of sensor samplng, t can be dvded nto two types: transmsson and reflecton. Reflecton can be appled n any place, because the measurng method of ths type s not lmted by sensor placement. In some places, t wll have a better applcaton prospect [1]. For the current ntellgent phones, only reflecton can be used due to the lmtaton of hardware. Wth the popularty of ntellgent phones, more and more physologcal parameters of montorng occur, whch are based on the platform of ntellgent phones, such as the heart rate and oxygen saturaton of montorng software. However, the man problem the software s faced wth now s that the data s not accurate because of the nose n measurement. Therefore, the thess manly focuses on how to mprove the montorng accuracy of oxygen saturaton and puts forwards that the key to guarantee the accuracy manly depends on the de-nosng algorthm n montorng. At the same tme, wth the contnuous development of modern medcal technologes, the dgtal mage processng plays an ncreasngly mportant role n the clncal dagnoss and treatment. In ths thess, the wrter apples the technque of wavelet threshold de-nosng nto oxygen saturaton montorng, and studes whch method of de-nosng algorthm s more accurate to the sgnal processng of oxygen saturaton. 2 THE THEORY OF MONITORING THE OXYGEN SATURATION ON PHONES Accordng to the reflecton theory of montorng oxygen saturaton [2], two dfferent wavelengths of lght are needed to measure the oxygen saturaton, whch should take turns to llumnate fngers. However, current moble phones cannot meet the demand. So the alternatve ISBN: SDIWC 1

2 soluton can only be used nstead. On the bass of the composton prncple of lght, the lght s composed of R (red), G (green) and B (blue) of a certan proporton. In ths case, the whte lght smulaton of your phone can be decomposed nto two knds of lght source. The specfc operaton s to use a whte lght to separate the two components of RGB mage as a result of the two types of lght rradaton. Upon the expermental analyss, the fnger mage B component proporton s less, so the R and G components of the mage should be chosen as a result. Then t also needs to calculate the ntensty change rate of each lght, and the result of the varaton of the lght ntensty s a rato. It deals wth each component of the average gray level mage processng n every frame, gettng the average gray value, gvng the whole vdeo mage average gray value of the waveform, and fndng out the peaks and troughs. Accordng to the prncple of AC, t s a part of the changes under the condton of lght, and the secton of DC s stll the same. Therefore, t can be concluded that the dfference between the peaks and troughs can be used as the value of the AC part corresponds, and the trough as the DC corresponds. Fnally, we can draw out the ntensty change rate of each lght. From what has showed, the specfc procedures of moble montorng of oxygen saturaton are as follows: sgnal acquston and quantfcaton, wavelet threshold de-nosng, the determnaton of peaks and troughs and oxygen saturaton calculaton. 3 WAVELET THRESHOLD DE-NOISING When completng sgnal acquston and quantfcaton, certan nose n each frame mage of the components n the drawng of the average curve stll exsts. And the nose should be elmnated completely. Durng sgnal acquston and converson, the nose affects by varous types such as nose nterference nevtably, the nterference results n lower of sgnal and even mpacts on the accuracy of calculaton. Common sources of nose manly have the followng two knds [3]: the frst s an electrcal nose, whch s a result of the human actvty, muscle tenson. Because of the wde spectrum electromyography and mxed wth heart rate, t s dffcult to separate t wth the general form of flter. The second s the baselne drft, whch s caused by human respratory, physcal actvtes such as low frequency nterference. Slghtly acute lmb movement wll cause serous change waveform sgnal, whch wll largely affect the accuracy of calculaton. There are many other random nose and nterference envronment n Ecg sgnal acquston, such as addtve whte nose, polarzaton, nstrument nternal nose and so on. For the nose nterference, baselne drft and electrcal nose are the most mportant nterference sources, and they can remove or suppress the nose n the sgnal processng part. Therefore, the blood oxygen saturaton sgnal usually s featured wth randomness and the strong background nose, wth a nonlnear and nonstatonary and sngular pont more sgnal. When handlng t, the tradtonal methods, such as average flterng de-nosng and Fourer flterng de-nosng, have some defcences. The wavelet de-nosng can remove the nterference of nose [4]. transform s a new analyss method on tme - frequency sgnal (the tme - scale), featured wth the characterstcs of multresoluton, and t s a good way to depct the nonstatonary sgnal characterstcs, such as peak, breakpont, edge, step, etc. So n sgnal processng, wavelet analyss has become a very practcal tool. threshold de-nosng s a knd of nonlnear de-nosng methods. It can effectvely separate the nose, not only ts varous de-nosng methods, smple prncple, small amount of calculaton, but also n the sense of mnmum mean square error of the approxmate optmal, and can remove the nose and obtan good vsual effect. Its basc prncple s that the sgnal after wavelet transform has dfferent statstcal features: large ampltude n general gven prorty to wth the sgnal, the wavelet coeffcents of manly concentrated n hgh frequency, nose of the the very great degree n ampltude of the smaller wavelet coeffcents, scattered n all coeffcent after wavelet transform. Accordngly, threshold wavelet threshold s used to deal wth the nose sgnal of acquston, and nfluences on blood oxygen saturaton calculaton accuracy are crtcal. ISBN: SDIWC 2

3 3.1 The Threshold De-nosng Steps Blood oxygen saturaton sgnal belongs to one dmensonal sgnal, so a one-dmensonal sgnal wavelet threshold de-nosng process s generally dvded nto the followng three steps of wavelet decomposton, the wavelet coeffcent threshold quantzaton and wavelet reconstructon [5]: Step 1: One-dmensonal sgnal wavelet decomposton. Choose a wavelet and determne a wavelet decomposton level N, then the one-dmensonal sgnal N layer of wavelet decomposton, obtanng the wavelet coeffcents. Step 2: coeffcent threshold quantzaton. To estmate nose, for each layer of hgh frequency coeffcent, choose a quantzed treatment to the threshold, and gan new wavelet coeffcents. Step 3: Reconstruct after de-nosng of onedmensonal sgnal. Based on wavelet decomposton N layer of low frequency coeffcent, deal wth the quantfcaton of 1 ~ N layer of hgh frequency coeffcent, and then assure one dmensonal sgnal wavelet nverse transformaton of wavelet reconstructon. 3.2 Selectng Functons From what has been mentoned above, the frst step of the wavelet threshold de-nosng s to select the wavelet and de-composte. At present, there are a lot of wavelet s, and dfferent wavelets have ther own dfferent advantages. Unlke Fourer analyss, wavelet analyss based on wavelet s does not only exst, but also meets the condtons of wavelet used as a wavelet. Dfferent applcaton envronments can choose dfferent wavelet s. As the mage processng, wavelet symmetry s used to avod the phase shft n mage processng. Dsappeared matrx order wavelet transform the energy s more concentrated, whch s useful n compresson. Good regularty n sgnal reconstructon s smooth [6]. Most of the applcatons choose a set length between 5 ~ 9 wavelet, because a set too long wll produce boundary problems, and a set too short wll lead to sgnal energy concentraton. In general, the longer the set s, the hgher the regularty. For nstance, Haar wavelet s strctly tght wth orthogonal wavelet, but t has the worst regularty. Daubeches wavelet has good orthogonalty, sex and regularty, whle symmetry s even not good. Coflet wavelet not only guarantees the dsappearance of the hgh dstance but also has good symmetry. Ths s under the sacrfce the set length acheved; Symlets n keepng the Daubeches wavelet under the premse of good propertes. Borthogonal wavelet s the Borthogonal wavelet, whch possesses the advantages that orthogonal wavelet does not have, whch can accurately reconstruct the sgnal. In ths thess, we wll test the above four knds of commonly used dscrete wavelet department: Symlets wavelet seres, Daubeches wavelet seres, Coflet wavelet and Borthogonal wavelet seres. Ths can guarantee the excellent characterstcs of wavelet taken nto consderaton, and t s more reasonable to be sutable for processng the heart rate sgnal wavelet. 3.3 Selectng Threshold Functons Threshold s dvded nto soft threshold and hard threshold, and w s the sze of the wavelet coeffcents. The threshold of wavelet coeffcents s sze, and lambda s the threshold. (1) Hard threshold When the absolute value of the wavelet coeffcent s smaller than a gven threshold, t s zero. Whle t s greater than the threshold, remanng unchanged, as shown n (1) w, w 0, (1) (2) Soft threshold When the absolute value of the wavelet coeffcent s smaller than a gven threshold, t s zero. Whle t s greater than the threshold, makng ts have mnus the threshold value, as shown n (2) : [ sgn( w)]( ), w 0, (2) Ths threshold method acheves good results n practce, but t stll exsts some potental dsadvantages. For example, hard threshold s dscontnuous at lambda, and refactorng may produce some oscllaton. Because soft threshold ISBN: SDIWC 3

4 estmaton and decomposton of wavelet coeffcents wavelet coeffcents are constant devaton, they drectly affect the reconstructed sgnal approxmaton degree of real sgnals. The wavelet threshold lambda has played a decsve role n de-nosng. It s common wth fxed threshold threshold value (Sqtwolog threshold), Sten unbased lkelhood estmaton threshold (Rgrsure threshold), heurstc threshold (Heursure threshold), the mnmax threshold (Mnmax threshold), Penalzed threshold, etc. 4 EXPERIMENT S RESULTS AND ANALYSIS In order to compare the de-nosng results, the paper ntroduces the sgnal-to-nose rato () and root means square error () as evaluaton ndex, and the expresson, as shown n (3) and (4) : N 2 N 2 10*lg( x / ( x ) ) (3) 1 1 x N 1 2 ( x x ) N (4) Among them, x s the orgnal sgnal n the I moment of samplng value. For the moment after de-nosng value, N s the number of samplng. In ths paper, the experment s dvded nto two stages. The frst phase s the applcaton of experments on dfferent wavelet, parameter value sgnal-to-nose rato and root mean square error of the oxygen saturaton sgnal; the second phase s based on phase a choce of wavelet threshold de-nosng algorthm based on a moble blood oxygen saturaton montorng applcaton software, selectng 10 n the restng state through professonal nstrument and the desgn of the software at the same tme, the measurement of blood oxygen saturaton to compare the accuracy of the software. In the frst stage, for ten vdeo real waveform, each cycle for 20 seconds of fnger mage R component of the waveform and a total of 600 samplng ponts, wth the ad of MATLAB tool, usng four knds of commonly used dscrete wavelet department: Coflet wavelet seres, Daubeches wavelet seres, Symlets wavelet and Borthogonal wavelet s more de-nosng effect, and take ten groups of averages. In the experment, use a soft threshold de-nosng method and select 3 as layers of wavelet decomposton. Accordng to the propagaton characterstcs of nose n dfferent wavelet layer, the nose varance estmaton parameters set as herarchcal estmaton method. Next, accordng to the four classcal wavelet algorthm and the selecton of sx common threshold de-nosng performance results contrast test. Accordng to the data n table 1, you can see that Coflet 5 wavelet algorthm not only guarantees the dsappearance of the dstance and has good symmetry, and the root means square error s small, showng that the most effectve denosng sgnal s preserved, and ensure the denosng of waveform and the smlarty between the orgnal sgnals. Table 1. Four knds of commonly used wavelet de-nosng performance comparson Coflet RMS E Daubeches Cof Db Cof Db Cof Db Cof Db Cof Db Symlets RMS E Db Borthogonal Sym Br Sym Br Sym Br Sym Br Sym Br Sym Br Seen from table 2, on the threshold selecton, selecton heurstc threshold, namely Heursure threshold waveform not only has good smoothness and the orgnal waveform curve has good smlarty. Therefore, when choosng the Coflet 5 cases of wavelet, use Heursure threshold selecton method s better. Table 2. Four knds of commonly used threshold de-nosng performance comparson ISBN: SDIWC 4

5 Threshold Sqtwolog Rgsure Heursure Mnmax In the second phase, choose 10 people who are from 18 to 50 years old. In quet cases, test them 10 tmes for the blood oxygen saturaton respectvely. Each test lasts for 1 mnute, and then record software measurement of blood oxygen saturaton value and professonal nstruments measured values. Everyone should complete the test, 10 tmes of calculaton when usng the software each person average per mnute blood oxygen saturaton measurement values and professonal nstrument measured the blood oxygen saturaton value. Seen from the data n table 3, the usage of ths artcle puts forwards that the Coflet5 wavelet threshold algorthm and Heursure moble blood oxygen saturaton montorng software developed by threshold value of the average accuracy s 98.97%, n lne wth expectatons. Table 3. Moble blood oxygen saturaton montorng compared wth actual blood oxygen saturaton montorng results Relatve No. Actual Estmated Relatve Error Accuracy % 0% % 1.05% % 0% % 1.03% % 2.06% % 2.08% % 1.01% % 2.04% % 1.06% % 0% on the Androd operatng system oxygen saturaton montorng applcaton software, applyng Coflet5 wavelet threshold de-nosng algorthm and heurstc threshold for sgnal denosng. Several strct experments show that ths algorthm can mprove the accuracy of blood oxygen saturaton montorng, and the precson beyond 90% s expected, reachng 98.97%, whch can help the user to understand ther own health more accurately. 6 REFERENCES [1] YAN Xn-zhong, YANG Jng and GUO Lue, Study on Measurng of Human Oxygen Saturaton, Chnese Journal of Medcal Devce, 2005, pp [2] YU We, GU Qng-en and YAO Xang, Expermental study on non-nvasve detecton of reflectance oxygen saturaton, Chnese Medcal Equpment Journal, 2007, pp [3] WEI Gao, Denosng algorthm of ECG sgnals based on wavelet transform, Chna Medcal Equpment, 2010, pp [4] Luo Qang, Tan Huame, Luo Png, Chen Q. De-nosng of ECG Sgnal Based on Statonary Transform[J]. Computer and Dgtal Engneerng, 2006, pp [5] LI Zh-qang, LI Tan-ru. Applcaton of Threshold Denosng n CRH Tran Data Processng, Computer Engneerng, 2011, pp [6] WANG Shu-zhang, JI Xao-png and YAN Wenjuan, The Extracton of Texture Features Based on Transform, SCI-TECH Informaton Development & Economy, 2008, pp CONCLUSIONS Montorng method of blood oxygen saturaton attracts much attenton n the research feld, whch s based on ts convenence, flexblty, smplcty, practcal, zero cost and other characterstcs. In ths thess, on the bass of predecessors research, the wrter uses mage processng technology and desgns a model based ISBN: SDIWC 5