A Novel Approach for Fetal ECG Extraction Blood Pressure Patient Using Adaptive Neuro-Fuzzy Inference Systems Trained With PSO

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1 Internatonal Journal of Scentfc Research Publcatons, Volume 3, Issue 3, March 03 A Novel Approach for Fetal ECG Extracton Blood Pressure Patent Usng Adaptve Neuro-Fuzzy Inference Systems Traned Wth PSO R.Anurekha *, A.svasankar ** * Student, M.E Embedded System Technologes ** Assocate Professor, Department of ECE, Krshnasamy College of Engneerng Technology, Cuddalore Abstract- Fetal ECG s an mportant parameter n medcal feld. Fetal Electrocardogram (FECG) dentfes the congental heart problems at the earler stage. Fetal Electrocardogram (FECG) sgnal s extracted from blood pressure mother s abdomen. FECG sgnal s recorded at the thoracc abdomnal area of blood pressure mother s skn. The thoracc ECG s consdered to be completely maternal ECG (MECG) of blood pressure mother. The abdomnal ECG s consdered to be a combnaton of blood pressure mother s ECG sgnals foetus s ECG sgnals rom nose. The maternal component of abdomnal ECG s a nonlnear transformed verson of the Maternal ECG. The method Adaptve Neuro-Fuzzy Inference System (ANFIS) s used to dentfy the nonlnear transformaton of maternal ECG. For dentfyng the nonlnear transformaton the FECG s extracted by subtractng the non lnear verson of the MECG sgnal from the abdomnal ECG sgnal. ANFIS s traned wth partcle swarm optmzaton for better qualty of sgnal. Ths method can be valdated on both real synthetc ECG sgnals. The results demonstrate the effectveness of extractng the FECG from blood pressure mother s maternal ECG. Index Terms- Fetal Electrocardogram, Adaptve neuro fuzzy nference system, partcle swarm optmzaton. D I. INTRODUCTION urng pregnancy perod, mother havng blood pressure, t can also affect the fetus. Care should be taken to prevent the fetus from mother s blood pressure. Fetal Electrocardogram (FECG) sgnal s used to montor the health condton of fetus by physcans. It s used to fnd heart problem at the early stage take better acton n crtcal stuaton. Fetal Electrocardogram gves the electrcal sgnal of fetal heart. There are two method to obtan Fetal Electrocardogram sgnal such as nvasve method non nvasve method. In nvasve method, FECG sgnal s recorded from electrode whch s placed on fetus head through mother s womb. Ths method may leads to problem for mother (bloodshed) fetus (nfecton). Comparng to frst method, second method s best t can be used nowadays. Fgure : Non-nvasve method recordng of AECG sgnal In non nvasve method, two set of electrodes are placed on blood pressure mother s body. The frst set of electrode s placed on the thoracc area. Ths gves the orgnal maternal ECG (MECG). The second set of electrode s placed on the abdomen area of the blood pressure mother. Fgure demonstrates the AECG sgnal recordng. Ths AECG sgnal contans Fetal ECG wth the altered MECG along wth other contamnated noses. The other contamnated noses are maternal electromyogram (EMG) sgnal, baselne werng, powerlne nterference, electrodes nose recordng system nose. Powerlne nterference, electrodes recordng system noses are elmnated by usng low nose amplfer. The Electromyogram (EMG) sgnal can be elmnated by usng classcal low pass flterng technques. The wavelet based methods are used to reduce the baselne werng. Therefore, t s safe for elmnatng altered maternal ECG component n the combned sgnal; estmated FECG sgnal can be obtaned. Many sgnal-processng technques are ntroduced to extract the FECG sgnal. These technques nclude adaptve flters [], correlaton technques [3], Adaptve nose cancellaton [5], sngular-value decomposton (SVD) [], wavelet transform [8], [], neural networks [7], blnd source separaton (BSS) [5] ndependent component analyss (ICA) s consdered among the most recent successful methods used for FECG extracton [8]. ICA requres multple electrodes for successful separaton of the FECG. Practcally, t s dffcult to predct the sgnal components n the abdomnal sgnals. In ths paper, we ntroduced ANFIS network traned wth PSO method for estmatng the FECG sgnal from one abdomnal ECG sgnal one thoracc ECG sgnal. We use ANFIS network to dentfy the nonlnear algn of thoracc MECG mxed wth the abdomnal ECG sgnal. Ths nonlnear transformaton between the two sgnals allows for cancellng the maternal component from the

2 Internatonal Journal of Scentfc Research Publcatons, Volume 3, Issue 3, March 03 abdomnal sgnal hence offers an estmate of the FECG sgnal. We show the results on synthetc ECG data real ECG data. Ths paper s organzed as follows: The followng secton, we wll analyss the schema of our work theory of ANFIS II. EXTRACTING FECG SIGNAL THROUGH NON- INVASIVE METHOD The objectve of non-nvasve method s to extract FECG sgnal from sgnal recorded at blood pressure mother abdomen (AECG). AECG sgnal contans FECG sgnal, altered MECG sgnal noses. The maternal components are dstorted because t s travel from mother s heart to abdomen. Ths type of dstorton can be taken as non-lnear transformaton of MECG sgnal. In order to mprove the result of FECG sgnal, we need to weaken the power of MECG sgnal decrease the effects of noses. We should able to recognze the altered MECG sgnal. The am purpose of proposed method s to dentfy the non-lnear transformaton. By ndentfyng the non-lnear transform t s appled on MECG sgnal whch s recorded n thoracc regon, we can obtan the estmaton of MECG sgnal n blood pressure mother s abdomen regon. By subtractng these MECG sgnal from AECG, we can get the FECG sgnal. Fgure shows the recordng of thoracc abdomnal sgnals. III. ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM Adaptve Neuro-Fuzzy Inference System s the combnaton of artfcal neural networks fuzzy nference systems. The complex system requres more sophstcated tool to model the system behavour. Mathematcal tool s not an approprate tool for modellng the system. By contrary, Fuzzy nference system can able to model the qualtatve aspects of human knowledge by usng fuzzy f-then rules. Ths type of fuzzy modellng was proposed by Takag sugeno. For better understng, we need some basc aspects of ths approach. Therefore, J.-S.R.Jang prescrbed a new method called ANFIS. The purpose of ANFIS s to automatcally realze the fuzzy system by usng the neural network. In ANFIS, Fuzzy Sugeno models are nvolved n framework of adaptve system to facltate the learnng adaptaton method. ) ANFIS structure The ANFIS approach learns the rules membershp functons from data. ANFIS s an adaptve network. An adaptve network s network of nodes drectonal lnks. Assocated wth the network s a learnng rule. It s called adaptve because some, or all, of the nodes have parameters whch affect the output of the node. These networks are learnng a relatonshp between nputs outputs. The ANFIS archtecture s shown n Fgure 3. The crcular nodes represent nodes that are fxed whereas the square nodes are nodes that have parameters to be learnt. Fgure. Recordng of thoracc abdomnal sgnals Ths method uses two recorded sgnal, one s recorded at thoracc regon m(n) another one s recorded at abdomen regon a(n)of blood pressure mother. Fgure summarzed the followng equatons: a(n)=m ~ (n)+f(n)+n(n) m~(n)=t{m(n)} where m(n) a(n) are the sgnals recorded at thoracc abdomnal areas respectvely. n(n) ndcates the sum of noses n the recorded sgnal. m~ (n) s the dstorted verson of m(n) sgnal due to non-lnear transformaton T. m~ (n) represents altered MECG sgnal components n the recorded AECG sgnal. As mentoned above, the dstorton resulted from non-lnear transformaton s created because the sgnal s recorded far away from the sgnal s source. We use ANFIS network traned wth PSO for estmatng the non lnear transformaton. Ths transform wll operate on m(n) produce the sgnal m ~ (n), whch s algned wth dstorted maternal component a(n).by removng the algned maternal component n a(n ) then estmate the FECG sgnal from a(n). Fgure 3 ANFIS archtecture for frst order Takag-Sugeno model Two fuzzy f-then rules under Takag-Sugeno (TS) model are gven as follows: If x s A If x s A y s B THEN f px q y r y s B THEN f px q y r ) LAYER OF ANFIS STRUCTURE Layer The output of each node s: O, A for, O, B ( y) for 3,4

3 Internatonal Journal of Scentfc Research Publcatons, Volume 3, Issue 3, March 03 3 O, So, the s essentally the membershp grade for x y The membershp functons could be anythng but for llustraton purposes we wll use the bell shaped functon gven by A ( x) x c a a, b, c b Where are parameters of membershp functon. These are the premse parameters. Layer Every node n ths layer s fxed. Ths s where the t-norm s used to AND the membershp grades - for example the product: O, w A B ( y),, Layer 3 Layer 3 contans fxed nodes whch calculate the rato of the frng strengths of the rules: w O3, w,, w w Layer 4 The nodes n ths layer are adaptve perform the consequent of the rules: O w f w ( p x q y r ) 4, The parameters n ths layer ( p, q, r ) are to be determned are referred to as the consequent parameters. Layer 5 There s a sngle node here that computes the overall output: O 5, w f Ths then s how, typcally, the nput vector s fed through the network layer by layer. We now consder how the ANFIS learns the premse consequent parameters for the membershp functons the rules 3) ANFIS learnng method In ANFIS, tranng updatng the parameters s one of the man problems. There are number of possble approaches based on hybrd learnng algorthm whch uses a combnaton of gradent Descent Least Squares Estmaton (LSE). Ths provdes a very hgh level descrpton of algorthm. It can be shown that the network descrbed f the premse parameters are w w f fxed, the output s lnear n the consequent parameters. Splt the total parameter set nto: set of total parameters(s) s the sum of set of premse (nonlnear) parameters (S) set of consequent (lnear) parameters (S).So, ANFIS uses two pass hybrd learnng algorthm: Forward pass: Here Ss unmodfed S s computed usng a LSE algorthm. Backward Pass: Here Ss unmodfed S s computed usng a gradent descent algorthm such as back propagaton. So, the hybrd learnng algorthm used to adapt the parameters n the adaptve network. ANFIS can be traned by hybrd learnng algorthm, t has more complexty slow convergence tme. Ths complexty convergence tme can be reduced by usng Partcle swarm optmzaton (PSO) for tranng ANFIS. IV. PARTICLE SWARM OPTIMIZATION PSO s a robust stochastc optmzaton technque based on the smulaton of the socal behavor of brds wthn a flock. It was developed n 995 by James Kennedy Russell Eberhart. It uses a number of partcles that consttute a swarm movng around n the search space lookng for the best soluton. Each partcle s treated as a pont n a N-dmensonal space whch adjusts ts flyng accordng to ts own flyng experence as well as the flyng experence of other partcles. The PSO algorthm s gven as follows:. For ntalzaton, at t = 0, the swam P(0) = { P, P,..., Pk }. For, =, k, the poston of partcle P P(0), s rom wthn the hyperspace ntal velocty of partcle P s gven for each.(we assume that the swarm has k partcles. ). Evaluate the performance of each partcle, usng ts current poston s the ftness of partcle at tme step t. 3. Compare the performance of each partcle to ts best Performance: If, then 4. Compare the performance of each partcle to the global best partcle: If, then 5. Change the velocty vector for each partcle as follows: 6. Move each partcle to a new poston 7. Go to step, repeat untl convergence s acheved.

4 Internatonal Journal of Scentfc Research Publcatons, Volume 3, Issue 3, March 03 4 The rom numbers are defned as = rc = rc, where r, r U(0, ) c c are acceleraton constants. The effect of the rom varables on the partcle trajectores, asserted that c + c 4 [5]. If c + c > 4, veloctes postons explode toward nfnty. ) ANFIS Traned wth PSO Two parameters of ANFIS are premse consequent parameters that are needs to be updated. Each parameter has three sets of values that s (a,b, c ) (p, q, r ).Each premse parameter has N genes the consequent parameter has (I+).R genes where N s the number of membershp functons, R s the number of rules appled I s the dmenson of nput data. Intally, parameters are consdered romly then these values are appled to PSO algorthm for updatng the values. Each teraton, one parameter s updated. In fnal stage, optmsed value of parameters wll be obtaned for each tranng par. V. PROPOSED ALGORITHM FOR FECG SIGNAL EXTRACTION In our proposed method, we uses two sgnals to extract FECG sgnal, one s m(n) another s a(n).these sgnals are segmented nto N-samples along wth overlappng frames for ANFIS tranng. The overlappng frames are consdered as N/ samples. The ANFIS nputs are vector whose values are obtaned from m(n)framng process. The ANFIS output s the frame obtaned from a(n).these parameters are adjusted separately for each par of vector. After tranng each par of vector s gven as ANFIS nput. The desred output s transformed verson of m(n) that s m ~ (n). Now, FECG s otaned by subtractng a(n) from m~(n). VI. RESULT AND DISCUSSION To extract the FECG sgnal from our proposed algorthm. The result of proposed algorthm s tested on both synthetc real sgnal. For constructng the tranng data, each of thoracc abdomen sgnal can be framed wth overlappng sgnal. We can synthesze the thoracc abdomnal sgnals for comparng the performance of proposed algorthm. Fgure 4 5 shows the results of proposed algorthm on synthetc ECG real ECG sgnals respectvely. Fgure 4. Proposed algorthm result for synthetc sgnal (a) synthetc abdomnal ECG (b) synthetc thoracc MECG (c) synthetc estmated thoracc MECG (d) synthetc extracted FECG Fgure 5.proposed algorthm result for real sgnal (a) real abdomnal ECG (b) real thoracc MECG (c) real estmated thoracc MECG (d) real extracted FECG. The results of proposed algorthm can be compared based on quantty crteron whch s Percentage Root Mean Square Dfference (PRD), s used to determne the smlarty between orgnal FECG extracted FECG sgnal. Calculate the PRD values n the followng equaton: PRD = % where or rec s the parameter of orgnal extracted sgnal. Table contan PRD values of proposed algorthm. Table. Comparng the performance of the proposed algorthm usng PRD values ALGORITHM PRD ANFIS ANFIS+PSO VII. CONCLUSIONS Ths paper presents ANFIS network for tranng PSO algorthm. It s used to extract fetal ECG sgnal from two recorded ECG sgnal at thoracc abdomen regon of blood pressure mother. Abdomnal ECG contan nonlnear transform verson of MECG Ths non lnear transform of MECG can be determned by usng ANFIS. The tranng method of ANFIS can affect the effcency of sgnal. So, we can use PSO as a new tool for tranng ANFIS network. The result of usng PSO s to reduce complexty fast convergence. To fnd the non lnear transform of MECG by removng the altered MECG from AECG then get good approxmaton FECG sgnal.

5 Internatonal Journal of Scentfc Research Publcatons, Volume 3, Issue 3, March 03 5 REFERENCES [] T. Takag M. Sugeno, Fuzzy dentfcaton of systems ts applcatons to modellng control, IEEE Trans. Syst., Man, Cybern., 5 985, 6-3. [] E. R. Ferrara, B. Wdrow, Fetal electrocardogram enhancement by tmesequenced adaptve flterng, IEEE Trans. on Bomed. Eng., 9, 98, [3] S. Horner, W. Holls, P.B. Crlly. Non-nvasve Fetal Electrocardograph Enhancement, IEEE Trans. On Bomedcal Engneerng /9, 99. [4] J. Shng, R. Jang, ANFIS: Adaptve network based fuzzy nference system, IEEE Trans. Syst., Man, Cybern., 3(3), 993, [5] Khamene, S. Neghdarpour, A new method for extracton of fetal ECG from the composte abdomnal sgnal, IEEE Trans. on Bomed. Eng., 47(4),000, [6] V. Zarzoso, A. N, Non-nvasve fetal electrocardogram extracton :Blnd separaton versus adaptve nose cancellaton, IEEE Trans. on Bomed.Eng., 48(), 00, -8. [7] G. Camps, M. Martnez, E. Sora, Fetal ECG extracton usng an FIR neural network, Computers n cardology, 00, [8] V. Vgneron, A. Paraschv-Ionescu, A. Azancot, O. Sbony, C. Jutten, Fetal electrocardogram extracton based on non-statonary ICA wavelet denosng, presented at the Int. Symp. Sgnal Processng ts Applcatons (ISSPA), Pars, Jul [9] G. Camps, M. Martnez, E. Sora, R. Magdalena, J. Calpe, J. Guerrero,Foetal ECG recovery usng dynamc neural networks, Art. Intel. Med., 3(3),004, [0] J. Kennedy R.C. Eberhart, Partcle Swarm Optmzaton, Proceedngs of the IEEE Internatonal Conference on Neural Networks,4, 995, [] Mara G. Jafar, Jonathon A. chambers, Fetal electrocardogram extracton by sequental source separaton n the wavelet doman, IEEE Transactons on Bomed. Engg.,5( 3), 005, [] Hamd Hassanpour, Mostafa Mesbah Boualem Boashash, Tme- Frequency Feature Extracton of Newborn EEG Sezer Usng SVD-Based Technques, EURASIP Journal on Appled Sgnal Processng, 004, [3] J. H. V. Bemmel, Detecton of weak fetal electrocardograms by autocorrelaton cross-correlaton of envelopes, IEEE Trans. Bomed. Eng., vol. BME-5, pp. 7 3, Jan. 968 [4] Eberhart, R. C. Kennedy, J. A new optmzer usng partcle swarm theory. Proceedngs of the Sxth Internatonal Symposum on Mcromachne Human Scence, Nagoya, Japan. pp , 995 [5] J. Kennedy, the behavour of partcle swarm n VW, Internatonal conference on evolutonary programmng, 988, [6] L. Lathauwer, Database for the dentfcaton of systems: FECG data EAST/SISTA K.U. Leuven, Belgum [Onlne].Avalable: [7] Non-Invasve Fetal Electrocardogram Database, avalable at: AUTHORS Frst Author R.Anurekha, Student,M.E Embedded system technologes Second Author A.svasankar, Assocate Professor, Department of ECE, Krshnasamy college of Engneerng Technology, Cuddalore, anuece88@gmal.com