ARTICLE IN PRESS Biomedical Signal Processing and Control xxx (2011) xxx xxx

Transcription

1 Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx Contents lsts avalable at ScenceDrect Bomedcal Sgnal Processng and Control journa l h omepage: Dscovery of multple level heart-sound morphologcal varablty resultng from changes n physologcal states Svetlana Kofman a, Amta Bckel b, Are Etan b, Atala Wess b, Noam Gavrely c, Nathan Intrator a, a School of Computer Scence, Tel-Avv Unversty, Tel-Avv, Israel b Department of Surgery, The Western Gallee hosptal, Naharya, Israel c Rappaport Faculty of Medcne, Technon-Israel Insttute of Technology, Hafa, Israel a r t c l e n f o Artcle hstory: Receved 12 December 2010 Receved n revsed form 19 June 2011 Accepted 5 August 2011 Avalable onlne xxx Keywords: Phonocardography Cluster analyss Classfcaton Cardac montorng Cardopulmonary nteracton a b s t r a c t Heart sounds carry nformaton about the mechancal actvty of the cardovascular system. Ths nformaton ncludes the specfc physologcal state of the subject, and short term varablty related to the respratory cycle. The nterpretaton of the sounds and extracton of changes n the physologcal state, whle montorng short term varablty s stll an open problem and s the subject of ths paper. We present a novel computatonal framework for analyss of data wth mult-level varablty, caused by externally nduced changes. The framework presented ncludes an ntal clusterng of the frst heart sound (S1) accordng to the morphology, and further aggregaton of clusters nto super-clusters. The clusters and super clusters are two methods of data segmentaton, each reflectng a dfferent level of varablty n the data. The framework s appled to heart sounds recorded durng laparoscopc surgeres of sx patents. Procedures of ths knd nclude anesthesa and abdomnal nsufflaton, whch together wth the respratory cycle, nduce changes to the heart sound sgnal. We demonstrate a separaton of the heart sound morphology accordng to dfferent physologcal states. The physologcal states consdered are the respratory cycle, and the stages of the surgery. We acheve results of 90 ± 4% classfcaton accuracy of heart beats to operaton stages. The proposed framework s general and can be used to analyze data characterzed by mult-level varablty for varous other (bomedcal) applcatons Elsever Ltd. All rghts reserved. 1. Introducton The heart sounds are generated by blood flow and closure of valves nsde the beatng heart. The heart sound morphology changes due to a complex nterplay between pressure gradents n atra, ventrcles and arteres. These affect the tmng, magntude and morphology of the produced heart sounds [1]. The resultng non-statonary sgnal can ndrectly reflect the physologcal state of the subject. It changes due to alteratons n bodly state and s constantly affected by the respratory cycle and the presence of nose. Abdomnal nsufflaton performed durng laparoscopc surgeres and the respratory cycle, are two processes affectng the heart sound morphology. The frst changes the pressure gradents n the large vens between those located n the abdomnal cavty (nferor vena cava) and n the lower lmbs, thus affectng venous return to Correspondng author at: School of Computer Scence, Faculty of Exact Scences, Tel-Avv Unversty, P.O.B , Tel-Avv 69978, Israel. E-mal address: nn@tau.ac.l (N. Intrator). the heart. The second changes the pressure gradents n the lungs. Both consequently affect the heart sound morphology [2,3]. When tryng to recognze changes n heart sounds that are related to pathology, we encounter the problem of separatng changes that occur due to the respratory cycle from pathologcal events. The goal of ths research s to buld a clusterng/classfcaton framework that can handle both types of morphologcal changes and produce a robust predcton of the physologcal state, ndependently of the large varablty of heart sounds. The heart sound s perhaps the most tradtonal bomedcal sgnal, as ndcated by the fact that the stethoscope s the prmary nstrument carred and used by physcans. Ths sgnal reflects mechancal changes n heart functonalty and provdes an ndcaton of the general state of the heart n terms of rhythm and contractlty. The phonocardogram s a recordng of the heart sound sgnal [4,5]. The heart sound sgnal or PCG sgnal of a normal heart s comprsed of two dstnct actvtes namely the frst heart sound, S1 and the second heart sound, S2 (Fg. 1). S1 occurs at the end of the sometrc contracton perod durng systole, and S2 occurs after the sovolumetrc relaxaton perod durng dastole [6] /$ see front matter 2011 Elsever Ltd. All rghts reserved. do: /j.bspc

2 2 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx Fg. 1. A phonocardogram recordng of a sngle heart beat, showng the two major heart sounds S1 and S2, as well as S3 and S4 (a), and a detaled descrpton of the nner structure of S1 and S2 (b), showng ther subcomponents [4]. The pulmonary system plays an mportant part n modulatng the cardovascular mechancal actvty by respratory-nduced changes of the pleural pressure, arteral resstance and venous return. Amt et al. observed sgnfcant dfferences between propertes of S1 and S2 occurrng durng nspraton and expraton [3]. The feld of automated analyss of the heart sound sgnals s relatvely new. Recent technologcal advances n dgtal electronc stethoscopes, acoustc sgnal processng and pattern recognton methods have made possble the desgn of algorthms for automated heart sound segmentaton and classfcaton [7]. Research n ths feld often focuses on two computatonal problems: the segmentaton of heart sounds nto heart cycles [8,9] and the recognton of the heart sound components (often only S1 and S2, and sometmes also S3, S4 and murmurs) [10,11], and classfcaton of heart sounds for recognzng cardac pathologes [12 14]. Much research was done on the classfcaton of dfferent heart sounds (HS), each representng a dfferent cardac pathology. Heart sounds are often preprocessed by convertng them to a tme-frequency sgnal representaton scheme. Methods such as short tme Fourer transform, Wgner Vlle dstrbuton, contnuous wavelet transform and reduced-nterference dstrbutons have been prevously appled on heart sound sgnals [8,15]. Classfcaton algorthms such as multlayer perceptron networks, learnng vector quantzaton (LVQ) [12,14], and clusterng analyss [13,16] were used to classfy the dfferent heart sounds. It was shown that t s possble to acheve a hgh classfcaton performance after a short tranng tme, and thus to carry out heart sound classfcaton n real-tme [8,9]. Work by Amt et al. [1], descrbed a framework for dentfyng dstnct morphologes of heart sounds and classfyng them nto physologcal states. Ths work focused on the effect of the respratory cycle and the respratory resstve load on the morphologes of S1 and S2. The framework presented n ths paper bulds on the analyss ntroduced by Amt et al. In ths work we extend the framework presented by Amt et al. Our framework analyzes heart sounds characterzed by mult-level varablty, caused by physologcal events as well as the respratory cycle. The extended framework enables a clear seperaton between morphologcal changes caused by the respratory cycle, and those caused by physologcal changes. We demonstrate that those physologcal changes, whch are extremely mportant for montorng cardac patents are better detected by the proposed method. The demonstraton s done on patents undergong laparoscopc surgeres. We further demonstrate that the method s applcable to other types of physologcal changes [17]. 2. Methods The followng computatonal framework analyzes data wth dfferent sources of varablty, caused by externally nduced changes. The analyss of heart sound that s characterzed by mult-level varablty s done usng extensve clusterng and then fusng several clusters nto super clusters based on sequental repetton of dfferent clusters n a localzed temporal regon. The cluster centers are then used to construct a new data representaton whch enables dentfcaton and assocaton of a new morphologcal sgnal nto ts correspondng physologcal state. The classfcaton framework s appled on the S1 component of the heart sound. The computatonal framework conssts of the followng buldng blocks (descrbed below n detal): 1. Preprocessng preparaton of a raw recorded sgnal for further analyss. Includes dgtal flterng of the acqured sgnal, segmentaton to cardac cycles and the extracton of the frst heart sound (S1). 2. Pattern recognton used to dentfy dstnct morphologcal patterns. The segmented S1 components are clustered usng an unsupervsed learnng method. 3. Feature extracton the data set of S1 components s transformed to a compact representaton of cluster dstance space. Each beat s then represented by a vector of dstances from the centers of sgnfcant clusters. 4. Classfcaton used to test the accuracy of the clusterng and to determne whether the dfferent sgnal morphologes revealed by clusterng represent dfferent physologcal states. 5. Super clusterng a super cluster aggregates a subgroup of clusters wthn a tme segment, to separate tme segments wth constant patterns of morphologcal behavor Expermental setup Heart sound sgnals from sx patents were recorded durng upper abdomnal laparoscopc surgery. Procedures of ths knd affect the cardovascular functon by reducng venous return and ncreasng systemc vascular resstance, consequently causng decreased cardac ndex, whch eventually affect heart sounds [18,19]. The phonocardogram sgnal was acqured from multple recordng locatons. Supplementary data, such as electrocardogram was acqured smultaneously. Fve patents underwent laparoscopc cholecystectomy surgeres and a sngle patent underwent a herna repar surgery. The study populaton conssted three males and three females, aged between 53 and 72. A detaled descrpton of the subjects s presented n Table 1. Recordng was done durng 3 dfferent phases of the surgery: followng nducton of anesthesa, durng pneumopertoneum and after abdomnal CO 2 desufflaton (end of pneumopertoneum). The patents were ntubated durng the surgery, meanng they had a constant respratory

3 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx 3 Table 1 Test subject descrpton. Subject Gender Age Cardac/vascular dseases Surgery S1 beats used BU F 53 None Lap. cholecystectomy 781 ZU M 55 None Lap. cholecystectomy 298 SO M 68 Ischemc heart dsease, hypertenson Lap. cholecystectomy 573 TA M 59 None Rep. herna 2931 NA F 60 Hypertenson Lap. cholecystectomy 657 HI F 72 Ischemc heart dsease Lap. cholecystectomy 778 rate. Each recordng took at least 30 s. The recordngs were class labeled accordng to stages of the surgery Sgnal representaton and preprocessng Detals of the phonocardogram recordng are provded n Amt et al. [16]. Followng amplfcaton, the heart sounds were fltered wth a dgtal band pass flter n the frequency range of Hz, where the bulk of the heart sound energy s found. The sgnal was then parttoned nto cardac cycles usng the peaks of the ECG-QRS complexes as reference ponts. The sgnal segment contanng the frst heart sound, S1, was defned from the begnnng of the QRS peak to 200 ms after the QRS peak. S1 sgnals were extracted from each cardac cycle, standardzed and aggregated for further processng. Segments wth peaks sgnfcantly bellow or above average were recognzed as nosy or nvald segments and were fltered out. The fltered heart sound sgnal and the S1 segments of t are shown n Fg Herarchcal clusterng Herarchcal clusterng s an unsupervsed learnng method, whch requres the user to specfy a measure of dssmlarty between (dsjont) groups, based on par-wse dssmlartes n the two groups. The result s a herarchcal representaton n whch the clusters at each level of the herarchy are created by mergng clusters at the next lower level. At the lowest level, each cluster contans a sngle observaton, at the hghest level there s only one cluster contanng all of the data. For recent advances n herarchcal clusterng, see [20]. The herarchcal clusterng used n ths work s agglomeratve, namely, t starts at the bottom of the herarchcal tree, and at each level merges the selected par of clusters nto a sngle cluster. The choce of the next two clusters to be combned s done usng group average crteron, whch chooses clusters such that the average dssmlarty between the groups s mnmal. The dstance between two clusters s defned by: d GA (G, H) = 1 d N G N j H G j H where N G, N H are the respectve number of observatons n each group, and d j parwse observaton dssmlarty [20]. Dssmlarty s measured usng a dstance metrc. The dstance metrc we used to compute the dstance between observatons s correlaton; for m, m j sgnals of length n we compute: t d j = m m j = 1 (m,t m l )(m j,t m j ), t (m,t m l ) 2 t (m j,t m j ) 2 where n m l = 1 m n,t t=1 To obtan the desred number of clusters the herarchcal clusterng tree s pruned. Observatons beneath each cut are assgned to a sngle cluster (Fg. 3) Clusterng and classfcaton framework The clusterng and classfcaton framework presented, dvdes S1 heart sound sgnals nto dstnct morphologcal groups. Each cluster represents a unque subclass of morphologes. The accuracy of the clusterng was tested on prevously unseen test data usng a classfcaton algorthm Clusterng procedure The nput to the clusterng procedure s a set of N heart sound cycles, B = {(b 1,l 1 ), (b 2,l 2 ),..., (b N,l N )}, where b s the representaton of a heart sound component (e.g. S1) durng a sngle cardac cycle, and l s the assocated class label l {L 1,..., L m }. The cluster analyss procedure assgns a cluster dentfer to each sgnal cycle, Fg. 2. (a) A phonocardogram recordng fltered n the frequency range of Hz. S1 segments are shown. (b) A smultaneous recordng of electrocardogram. Fg. 3. A herarchcal clusterng tree s graphcally represented by a dendogram. Ths llustrates the process of teratvely mergng smlar clusters, followed by prunng of the herarchcal tree to obtan four clusters (C 1,..., C 4).

4 4 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx usng the herarchcal clusterng algorthm, producng a clustered dataset C = {(b 1,c 1 ), (b 2,c 2 ),..., (b N,c N )}, where c {1,..., M} are arbtrary cluster dentfers. Usng ths notaton, a cluster C j s the set of sgnal cycles wth cluster dentfer c j : C j = { (b,c j ) C}. The center of a cluster C j s a weghted average of the clusters elements, n whch each sgnal cycle s weghted by ts smlarty to the clusters arthmetc mean: C j w C b, w = 1 D(b, ( b j C / C j ), where D s a dstance functon wth a maxmum dstance of 1 [16]. j Clusters that contan more than a certan mnmal porton of the heart beats n a label are denoted as sgnfcant clusters,.e. cluster C j s sgnfcant f there s a label L such that {b k (b k, L ) B and (b k, c j ) C} > {b k (b k, L ) B} ˇ. (In ths experment ˇ s set to 0.1.) Such a defnton of a sgnfcant cluster prevents the state when a class label has no representaton of ts domnant clusters n the sgnfcant clusters set due to ts small sze n relaton to other class labels. Insgnfcant clusters are created due to nose or short term, sngular physologcal events (e.g. the process of abdomnal nsufflaton causes the creaton of multple clusters). Those clusters are elmnated by mergng them nto sgnfcant clusters. Each beat b from an nsgnfcant cluster s moved to a sgnfcant cluster C j such that d j = D(b, C j ) s mnmal. Cluster centers are recalculated after the mergng process Feature extracton The centers of the sgnfcant clusters provde a compact representaton of the morphologcal varablty n the entre dataset. Furthermore, a sgnal beat b can be effcently characterzed by a vector of dstances from the centers of the sgnfcant clusters. d l = (d 1, d 2,..., d ˆM ), d j = D(b, C j ). The classfcaton algorthm s appled n ths new feature space [1] Classfcaton algorthm The classfcaton algorthm determnes the accuracy of the clusterng procedure. The algorthm attempts to predct class labels of heart beats n a prevously unseen test set, usng ther representaton n cluster dstance space. The data set B s dvded nto subsets B tran and B test. Only B tran s used n the clusterng procedure. B test s used to measure the correctness of the clusterng. The dvson of data set B to B tran and B test s performed separately for each class label L. The group B tran s defned as a subgroup of B tran, where all the heart beats belong to class label L (B test s defned smlarly). B tran s constructed n the followng way: the group B = {b k (b k, L ) B} s dvded to r (r = 5) equal subgroups, each... B r. Those contanng consecutve beats from B : B = B 1 B 2 subgroups are dvded equally between B tran and B tran = U r/2 ˇ2j 1, B test = U r/2 ˇ2j j=1 j=1 B tran = U m =1 Btran, B test = U m =1 Btest B test : For r = 5, B tran s 60% of the data set, and B test s the remanng 40%. The classfcaton algorthm used s k-nearest-neghbors. Ths s a smple, non-parametrc method, based on closest tranng examples n the feature space. Gven a query pont d 0 B test, and a set of labeled tranng ponts B tran, we fnd the k tranng ponts d l B tran, r = 1,..., k closest n dstance to d 0, and then classfy to label l0 {L 1,..., L m } usng majorty vote among k neghbors. Tes are broken at random [20]. Dfferent dstance metrcs were tested: 1. Eucldean D () d l d 0 = 2 ˆM k=1 (d k d 0 k )2 2. Mahalanobs based on correlatons between varables by whch dfferent patterns can be dentfed and analyzed. It dffers from Eucldean dstance n that t takes nto account the correlatons of the data set. Defned by D( d l, d j ) = ( d l d j )V 1 ( d l d j ) T where V the covarance matrx of s d and d j [20]. The clusterng and classfcaton outlne s summarzed below: Let B be the data set of a sngle patent. 1. Splt data set B s nto subsets B tran and B test. 2. Apply the clusterng procedure on the tranng set B tran, to produce a clustered data set C tran. 3. For each cluster n C tran, calculate cluster center C tran. j 4. Determne sgnfcant clusters. 5. Elmnate nsgnfcant clusters, by movng the heart beats n them to sgnfcant clusters. 6. Recalculate cluster centers C tran,..., C tran 1 of sgnfcant clusters M n C tran after the merge. 7. Transform the heart beats b B tran B test to a compact representaton d l = (d 1, d 2,..., d ˆM ) of dstances from the centers of sgnfcant tranng clusters. 8. Use KNN classfer to calculate the clusterng accuracy. For each beat b B test, n ts representaton n cluster dstance space, calculate KNN( d l ) = ll, ll {L 1,..., L m } 9. Calculate classfcaton accuracy of the patent by averagng the classfcaton accuracy of all the class labels: CA(L ) = {bj B test and (b j, L ) B lj = L } =1,...,m, CA = CA(L ) {b j B test and (b j, L ) B} m The above equaton gves each class label the same weght, dsregardng the number of heart beats n t Super clusterng As mentoned before, the morphologcal changes n data set B are caused due to two smultaneously occurrng physologcal processes: the respratory cycle and the laparoscopc surgery. Morphologcal changes caused by the respratory cycle manfest as cyclc transtons of adjacent heart beats between two or more clusters. Morphologcal changes that are caused by the laparoscopc surgery create a new set of clusters wth cyclc transtons between them (Fg. 7a). The purpose of the super clusterng s to dvde data set B nto physologcal states caused by the surgery alone. A super cluster s defned as a segment of tme, n whch the heart sound cycles are characterzed by a small constant set of alternatng morphologcal behavors. Ths pattern manfests as transtons between a small set of clusters, and appears as cyclc transtons between two or more clusters. The crteron for the end of a supercluster and the begnnng of the next s a change n the set of clusters adjacent heart beats belong to. The algorthm for parttonng the tme lne to super clusters s based on herarchcal clusterng. The nput to the algorthm s the clusterng result of heart sound cycles. C = {(b 1,c 1 ), (b 2,c 2 ),..., (b N,c N )}, where b B, s a data set of heart sound components (.e. S1), and c {1,..., M} are arbtrary cluster dentfers. C s ordered

5 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx 5 Fg. 4. Herarchcal clusterng results of 781 S1 beats. After extractng of the frst heart sound (S1), the beats were algned usng the tme-shft averagng algorthm wth respect to ther mean. Herarchcal clusterng s appled on the data wth cutoff at 40 clusters. Only sgnfcant clusters are presented. The sgnals wthn each cluster are morphologcally dstnct, and have a small varablty compared to other clusters. by the occurrence n tme of b. The resultng super-clusters SC are non overlappng subgroups of C. SC = {(b j, c j ),..., (b j+t, c j+t )} The super-clusterng algorthm begns wth the parttonng of C to wndows of constant sze d (d = 5). Each wndow contanng cluster dentfers: W = {W 1,..., W k }, W = {c d( 1)+1,..., c d( 1)+d } Herarchcal clusterng s appled on the data set of wndows W. The dstance metrc used to compare between wndows s Jaccard. The Jaccard coeffcent measures smlarty between sample sets, and s defned as the sze of the ntersecton dvded by the sze of the unon of the sample sets. The Jaccard dstance measures dssmlarty between sample sets, and s complementary to the Jaccard coeffcent. Jaccard dstance s obtaned by subtractng the Jaccard coeffcent from 1 [21]. D Jaccard (W, W j ) = 1 W W j W W j The choce of the next two clusters to be combned s done usng the average crteron. The result of the herarchcal clusterng algorthm s smoothed to obtan super-clusters conformng to the above defnton. 3. Results The clusterng and classfcaton framework was appled separately on the data set of each of the sx patents. The data set was constructed from a sngle recordng channel, chosen by ts recordng qualty. Only the S1 component of the heart sound cycles was used. The number of heart beats processed per subject ranged between 300 and Durng the preprocessng stage a mean of 10 ± 3.5% of S1 beats were removed due to nose. Each heart sound cycle was labeled by a class label descrbng the stage n the operaton t was recorded n ( before operaton, durng operaton, after operaton and ntermedate labels). The number of labels ranged from 3 to 5. The data set was dvded to tran and test. 60% of the S1 beats were used for tranng, and 40% for testng. Consecutve heart beats were selected to ensure the unbased representaton of beats occurrng n dfferent stages of the respratory cycle, snce there are respratory-nduced morphologcal varatons of S1 [1]. Herarchcal clusterng was appled on the tranng set, and the clusterng tree obtaned was pruned at 40 clusters. The number of clusters was decded upon emprcally. Experments wth less clusters showed results that were not senstve enough. Experments wth more than 40 clusters created too many small nsgnfcant clusters. The number of sgnfcant clusters vared from 7 to 13 among subjects. Sgnal averagng wthn each cluster exhbted a small morphologcal varablty compared to the varablty of sgnals n dfferent clusters, provdng a more accurate descrpton of the data (Fg. 4). K-nearest-neghbor classfcaton was used to test the accuracy of the clusterng. The algorthm attempts to predct class labels of heart beats n the prevously unseen test subset, usng ther representaton n cluster dstance space. The algorthm was appled wth K {1,3,5} and Eucldean and Mahalanobs dstance metrcs. The Mahalanobs dstance metrc was also used by Amt et al. [1] for the classfcaton of heart sounds. It sgnfcantly mproved predcton results n ths study as well. The value of K = 1 and the Mahalanobs dstance metrc gave best predcton results for the majorty of the subjects. Wth those parameters, the classfcaton success (CA) over

6 6 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx Fg. 5. (a) Three respratory cycles. The lowest pont of the cycle corresponds to the end of expraton and begnnng of nspraton, and the hghest pont to the end of nspraton and the begnnng of expraton. (b) Mappng of the respratory cycle to the range The graph shows a snus of ths map. The red ntervals are tmes of S1 beats. (For nterpretaton of the references to color n ths fgure legend, the reader s referred to the web verson of the artcle.) Fg. 6. (a) Classfcaton accuracy (CA) values for 6 subjects for data subsets B nspraton, B expraton and the entre data set. The X axs are the patents, the Y axs s the classfcaton accuracy n percents. We can see that for 5 of 6 patents the CA of subset B nspraton s slghtly hgher than the CA of all the data. (b) Mean classfcaton accuracy (CA) values for data subsets B nspraton, B expraton and the entre data set. Each bar s CA for fve patents. Patent HI was excluded from ths calculaton. We can see that the mean CA s slghtly better for B nspraton. 6 patents was 90 ± 4%. The CA of a patent s calculated by averagng the CA of all hs class labels. Ths gves each class label the same weght, dsregardng the number of heart beats n t. Detaled classfcaton results can be seen n Table 2. Table 2 Classfcaton performance of S1 sgnals usng the KNN algorthm. K Dstance metrc CA 1 Eucldean 85 ± 5 Mahalanobs 90 ± 4 3 Eucldean 82 ± 7 Mahalanobs 89 ± 6 5 Eucldean 78 ± 8 Mahalanobs 87 ± 7 Mean and standard devaton of the classfcaton accuracy for all subjects were calculated wth dfferent parameters of the K-nearest-neghbor classfcaton algorthm. The Mahalanobs dstance metrc gves sgnfcantly better results for all values of K. Best performance for K = 1. The super clusterng algorthm was further appled on the tranng subset. The clustered data, C tran, was parttoned to wndows of sze d = 5. The wndows were clustered usng herarchcal clusterng algorthm, wth Jaccard dstance metrc. The clusterng tree was pruned at 10 clusters for fve of the patents, and at 9 clusters for a sngle patent. The number of clusters was decded upon emprcally. The number of super clusters was between 5 and Respratory modulaton wthn a super cluster The respratory cycle modulates heart sound morphology. Heart beats durng hgh thoracc pressure (early expraton) are morphologcally dfferent from beats followng hgh negatve thoracc pressure (early nspraton) [16]. Ths physologcal behavor should be taken nto consderaton when analyzng heart sound clusters. Another physologcal process characterzng the data s pneumopertoneum, whch further changes thoracc pressures. Ths process affected the class labels of the data. The super-cluster s a tme segment n whch there are transtons of heart beats among a small set of clusters. Those transtons

7 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx 7 Fg. 7. (a) Clusterng progress. The X axs s 781 S1 beats ordered by tme of occurrence. Y axs are clusters. Each cluster dffers both n color and n value. (b) The dvson of the tme lne to super-clusters. We can see the correlaton between super clusters and class labels. (c) Hstogram of the respratory phase of the S1 beats n a super cluster. The X axs s n the range that maps the respratory cycle, the Y axs s the number of samples n each bar. Each color n the hstogram s a dfferent cluster wthn the super cluster. We can see that when there s more than one domnant cluster n a super cluster, each cluster belongs to dfferent respratory phase. (For nterpretaton of the references to color n ths fgure legend, the reader s referred to the web verson of the artcle.) can be explaned by the respratory cycle. An example of cyclc transtons can be seen n Fg. 7a. In the frst label Before operaton the S1 beats are classfed perodcally to both clusters 5 and 6. The morphologcal dfferences between them can be seen n Fg. 4. By showng that S1 beats n each of those clusters occur n dfferent stages of the respratory cycle, we show correlaton between a cluster and a respratory phase. The respratory sgnal s generated by the movement of the chest durng respraton. It can be extracted from the heart sound sgnal by flterng t usng a band pass flter n the range Hz. The breathng sgnal s dvded to respratory cycles. The lowest pont of the cycle corresponds to the end of expraton and begnnng of nspraton, the hghest pont to the end of nspraton and the begnnng of expraton. The respratory cycle s mapped to the range of 0 360, the snus cycle. Inspraton corresponds to values 0 180, and expraton to (Fg. 5). By examnng the respratory cycle stages of S1 beats n dfferent clusters wthn a sngle super-cluster (Fg. 7c), we observe that each cluster corresponds to a dfferent part of the respratory cycle, thus showng that the morphologcal dfferences wthn a super-cluster are caused due to respraton. The exstence of several super-clusters reveals morphologcal changes to the heart sounds that are caused by the alternatng thoracc pressure n dfferent stages of the operaton, caused by pneumopertoneum. Correlaton between the super-clusters and class-labels only emphaszes ths noton (Fg. 7b).

8 8 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx Fg. 8. Clusterng and super clusterng results for 298 S1 beats of patent ZU. Ths patent does not have a recordng from before the surgery, only a recordng from the nflaton stage tself and recordngs from dfferent states of the patent durng and after the surgery. For detaled explanaton see Fg. 7. (For nterpretaton of the references to color n ths fgure legend, the reader s referred to the web verson of the artcle.) 3.2. Applcaton of the framework to subsets of the data The respratory cycle nduces sgnfcant morphologcal changes to the heart sound sgnal [1]. Therefore, the dvson of the S1 beats nto subsets accordng to the respratory phase they occur n reduces the varablty of the data. We descrbe an attempt to mprove classfcaton accuracy by applyng the clusterng and classfcaton framework on subsets of the data, when the S1 beats n each subset belong to dfferent phases of the respratory cycle. The S1 components n data set B are mapped to stages of the respratory cycle as descrbed n Secton 3.1. Each S1 beat b receves a par (RP start, RP end ) such that RP start, RP end [0, 360], ndcatng the respratory phase t begns and ends n. The data set s dvded nto two subsets: 1. B nspraton heart beats b such that RP start, RP end [0, 180]. 2. B expraton heart beats b such that RP start, RP end [180, 360]. The clusterng and classfcaton framework was appled separately on each of the data sets. The same methodology was used for the subsets as for the entre data set. K-nearest-neghbor classfcaton algorthm was used wth K = 1 and Mahalanobs dstance metrc, parameters that were shown to mprove classfcaton results. The applcaton of the framework on the B nspraton data set showed slghtly better classfcaton accuracy (CA), for most subjects. For a sngle subject the CA was sgnfcantly lower for ths subset (Fg. 6a). Classfcaton results of B expraton dd not mprove CA. Mean classfcaton accuracy for both data subsets s presented n Fg. 6b. Snce the results are not unanmous among subjects, and the ncrease n classfcaton accuracy s small, we cannot conclude that the use of S1 beats from the nspraton stage alone s

9 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx 9 Fg. 9. Clusterng and super clusterng results for 573 S1 beats of patent SO. Ths patent has a recordng from the nflaton stage tself and recordngs from dfferent states of the patent after the surgery. For detaled explanaton see Fg. 7. (For nterpretaton of the references to color n ths fgure legend, the reader s referred to the web verson of the artcle.) recommended. More subjects and further studyng of data set dvson methods should be done n order to reach a decsve concluson. 4. Dscusson and conclusons We have proposed a framework for the analyss of a mult-level heart sound varablty. Our goal was to provde computatonal tools for recognzng morphologcal changes n the sgnal that occur due to pathologcal events, and are not related to the respratory cycle. The framework ntally clusters the frst heart sound (S1) accordng to ts morphology, and later aggregates clusters nto super clusters. The clusters and super clusters are two methods of data segmentaton, each reflectng a dfferent level of varablty n the data. The results demonstrate that sgnfcant morphologcal changes occur durng physologcal state changes and n each physologcal state; there are morphologcal changes that are due to the respratory phase. Clusters wthn a super cluster were often dstrbuted wthn the respratory cycle, the S1 beats n each cluster belongng to a dfferent range of respratory phases. The results further support the fndngs of Amt et al. [16] whch were found on healthy subjects, demonstratng that the phase of the respraton

10 10 S. Kofman et al. / Bomedcal Sgnal Processng and Control xxx (2011) xxx xxx cycle (nspraton or expraton), ndcated by the nstantaneous breathng pressure, has a marked effect on the morphology of the heart sound sgnal. Super clusters were found useful when cluster members dffer n more than one dmenson. Specfcally, the morphology of heart sounds depends on the physologcal condton of the patent (abdomnal pressure), and n each such physologcal condton t depends on the respratory cycle. The result of segmentng data to super clusters was characterzed by the dvson of a sngle class label nto several super clusters. Ths ndcates that super clusterng was able to uncover physologcal changes that are not marked by class labels. Class labels reflect only external observatons on the patent s state, and thus dd not accurately descrbe the full physologcal states durng the surgery. Stablty n morphology due to physologcal state was observed. The length of a super cluster vared from a few second durng frequent poston changes, to over 5 mn durng surgery. Ths ndcates that a constant physologcal state nduces a constant set of heart sound morphologes. The super clusterng algorthm was found to be an essental step n characterzng physologcal states based on morphologcal clusterng, as t enables us to separate the varablty of the morphology nto the two basc causes of that varablty. In summary, the framework we ntroduced analyzed heart sounds characterzed by mult-level varablty caused by physologcal events as well as the respratory cycle. It enabled a clear seperaton between morphologcal changes caused by the respratory cycle, and those caused by physologcal changes such as nsufflaton. Ths framework s general, and can be used to analyze dfferent knds of data characterzed by mult-level varablty n tme. In ths paper the framework was demonstrated on heart sounds recorded from patents undergong laparoscopc surgeres. In a thess by S. Kofman, t was also tested on a data set of heart sounds recorded from patents durng hemodalyss, and showed good classfcaton accuracy [17]. Due to ts general nature, the framework could also be used to analyze other bomedcal sgnals (Fgs. 7 9). References [1] G. Amt, Automatc Analyss of Vbro-Acoustc Heart Sgnals: Combnng Sgnal Processng and Computatonal Learnng Technques for Non-nvasve Estmaton and Montorng of cardac Functonalty, [2] R.W.M. Wahba, F. Beque, S.J. Kleman, Cardopulmonary functon and laparoscopc cholecystectomy, Can. J. Anaesth. 42 (1995) [3] G. Amt, K. Shukha, N. Gavrely, N. Intrator, Respratory modulaton of heart sound morphology, Am. J. Physol. Heart Crc. Physol. 296 (3) (2009) H796 H805. [4] M.E. Tavel, Clncal Phonocardography & External Pulse Recordng, 3rd ed., Year Book Medcal Publshers Inc., Chcago, [5] M. Rangayyan, Rangaraj, Bomedcal Sgnal Analyss, IEEE Press Seres n Bomedcal Engneerng, [6] T. Reed, Heart sound analyss for symptom detecton and computer-aded dagnoss, Smul. Model. Pract. Theory 12 (2) (2004) [7] W. Mynt, B. Dllard, An electronc stethoscope wth dagnoss capablty, n: Proceedngs of the 33rd IEEE SSST, 2001, pp [8] T. Olmez, Z. Dokur, Classfcaton of heart sounds usng an artfcal neural network, Pattern Recognt. Lett. 24 (2003) [9] C. Gupta, R. Palanappan, S. Rajan, S. Swamnathan, S.M. Krshnan, Segmentaton and classfcaton of heart sounds, n: CCECE/CCGEI, 2005, pp [10] C. Gupta, R. Palanappan, S. Swamnathan, S. Krshnan, Neural network classfcaton of homomorphc segmented heart sounds, Appl. Soft Comput. 7 (2007) [11] D. Gll, N. Gavrel, N. Intrator, Detecton and dentfcaton of heart sounds usng homomorphc envelogram and self-organzng probablstc model, Comput. Cardol. 32 (2005) [12] D. Boutana, M. Djedd, M. Bendr, Identfcaton of aortc stenoss and mtral regurgtaton by heart sound segmentaton on tme-frequency doman, n: Proceedngs of the 5th ISISPA, [13] Z. Dokur, T. Olmez, Heart sound classfcaton usng wavelet transform and ncremental self-organzng map, Dgt. Sgnal Process. 18 (2008) [14] T. Olmez, Z. Dokur, Feature determnaton for heart sounds based on dvergence analyss, Dgt. Sgnal Process. 19 (2009) [15] P.M. Bentley, P.M. Grant, J.T.E. McDonnell, Tme-frequency and tme-scale technques for the classfcaton of natve and boprosthetc heart valve sounds, IEEE Trans. Bomed. Eng. 45 (1) (1998) [16] G. Amt, N. Gavrely, N. Intrator, Cluster analyss and classfcaton of heart sounds, Bomed. Sgnal Process. Control 4 (2009). [17] S. Kofman, Dscovery of Multple Level Heart-sound Morphologcal Varablty Resultng from Changes n Physologcal States, [18] J.L. Jors, D.P. Norot, M.J. Legrand, Hemodynamc changes durng laparoscopc cholecystectomy, Anesth. Analg. 76 (1993) [19] S.Z. Goldhaber, G. Pazza, The acutely decompensated rght ventrcle: pathways for dagnoss and management, Chest 128 (3) (2005) [20] T. Haste, R. Tbshran, J. Fredman, The Elements of Statstcal Learnng, Sprnger, [21] C. Romesburg, Cluster Analyss for Researchers, Lulu Press, North Carolna, 1984.