Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier

Auscultation, the technique of listening to heart sounds with a stethoscope can be used as a primary detection system for diagnosing heart valve disorders. Phonocardiogram, the digital recording of heart sounds is becoming increasingly popular as it is relatively inexpensive. In this paper, a technique to improve the performance of the Least Square Support Vector Machine (LSSVM) is proposed for classification of normal and abnormal heart sounds using wavelet based feature set. In the proposed technique, the Lagrange multiplier is modified based on Least Mean Square (LMS) algorithm, which in turn modifies the weight vector to reduce the classification error. The basic idea is to enlarge the separating boundary surface, such that the separability between the clusters is increased. The updated weight vector is used at the time of testing. The performance of the proposed systems is evaluated on 64 different recordings of heart sounds comprising of normal and five different pathological cases. It is found that the proposed technique classifies the heart sounds with higher recognition accuracy than competing techniques.

[1]  Emre Çomak,et al.  A new training method for support vector machines: Clustering k-NN support vector machines , 2008, Expert Syst. Appl..

[2]  Goutam Saha,et al.  ON A ROBUST ALGORITHM FOR HEART SOUND SEGMENTATION , 2007 .

[3]  L. Sakari,et al.  A heart sound segmentation algorithm using wavelet decomposition and reconstruction , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[4]  L. Khadra,et al.  Time-Frequency Analysis of , 1996 .

[5]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Ts Leung,et al.  Heart sounds made easy , 2002 .

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  D. T. Barry,et al.  Time-frequency analysis of the first heart sound , 1995 .

[10]  Lawrence W. Solomon.  Yale University School of Medicine Heart Book , 1992, The Yale Journal of Biology and Medicine.

[11]  Leslie Cromwell.  Biomedical Instrumentation and Measurements , 1973 .

[12]  Davut Hanbay.  An expert system based on least square support vector machines for diagnosis of the valvular heart disease , 2009, Expert Syst. Appl..

[13]  I. Cathers,et al.  Neural network assisted cardiac auscultation , 1995, Artif. Intell. Medicine.

[14]  Samjin Choi.  Detection of valvular heart disorders using wavelet packet decomposition and support vector machine , 2008, Expert Syst. Appl..

[15]  Goutam Saha,et al.  Classification of heart sounds using empirical mode decomposition based features , 2008, Int. J. Medical Eng. Informatics.

[16]  M. Tavel,et al.  Cardiac auscultation. A glorious past--but does it have a future? , 1996, Circulation.

[17]  Douglas S. Moodie.  Cardiac Auscultatory Skills of Internal Medicine and Family Practice Trainees: a Comparison of Diagnosfic Proficiency , 1998 .

[18]  Tamer Ölmez,et al.  Classification of heart sounds using an artificial neural network , 2003, Pattern Recognit. Lett..

[19]  Nancy E. Reed,et al.  Heart sound analysis for symptom detection and computer-aided diagnosis , 2004, Simul. Model. Pract. Theory.

[20]  M. A. Chizner.  Cardiac auscultation: rediscovering the lost art. , 2008, Current problems in cardiology.

[21]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[22]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[23]  L. Durand,et al.  Digital signal processing of the phonocardiogram: review of the most recent advancements. , 1995, Critical reviews in biomedical engineering.

[24]  Goutam Saha,et al.  In search of an optimization technique for Artificial Neural Network to classify abnormal heart sounds , 2009, Appl. Soft Comput..

[25]  Seung Hong Hong,et al.  Comparison between short time Fourier and wavelet transform for feature extraction of heart sound , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[26]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[27]  Barry L. Zaret,et al.  Yale University School of Medicine Heart Book , 1992 .

[28]  Shankar M. Krishnan,et al.  Neural network classification of homomorphic segmented heart sounds , 2007, Appl. Soft Comput..

[29]  Vojislav Kecman.  Support Vector Machines , 2001 .

[30]  M. Silverman,et al.  A history of cardiac auscultation and some of its contributors. , 2002, The American journal of cardiology.

[31]  I. Hartimo,et al.  Heart sound segmentation algorithm based on heart sound envelogram , 1997, Computers in Cardiology 1997.

[32]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[33]  S Ari,et al.  DSP implementation of a heart valve disorder detection system from a phonocardiogram signal , 2008, Journal of medical engineering & technology.

[34]  S. Lukkarinen,et al.  A new phonocardiographic recording system , 1997, Computers in Cardiology 1997.

[35]  S. M. Debbal,et al.  Time-frequency analysis of the first and the second heartbeat sounds , 2007, Appl. Math. Comput..

[36]  Clarence Shub,et al.  Echocardiography or auscultation? How to evaluate systolic murmurs. , 2003, Canadian family physician Medecin de famille canadien.