Heart sounds analysis using wavelets responses and support vector machines

Over the last decade, computerized heart screening techniques have been increasingly receiving attention. In general, one can say that such techniques can be categorized as: with, or without the so-called Electrocardiogram (ECG) signal. Considering this latter strategy, we devote this paper with the intention to design an algorithm that provides with heart sounds known as Phonocardiograms (PGC) investigation for further definition of the present pathology if any. A novel algorithm for heart sounds segmentation is also presented. The decision making is accomplished by means of support vector machines (SVM) classifier which is fed by characteristic features extracted from PCGs basing on wavelet filter banks coefficients so that PCG signals are classified into five classes: normal heart sound (NHS), aortic stenosis (AS), aortic insufficiency (Al) mitral stenosis (MS), and mitral insufficiency (MI). The SVM was trained on a low-dimensional feature space, and tested on relatively a big dataset in order to show its generalization capability.

[1]  Kannan Balakrishnan,et al.  Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier , 2012 .

[2]  Sheng Chen,et al.  A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[3]  Wen-Chung Kao,et al.  Automatic heart sound analysis with short-time Fourier transform and support vector machines , 2009, 2009 52nd IEEE International Midwest Symposium on Circuits and Systems.

[4]  Goutam Saha,et al.  Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier , 2010, Expert Syst. Appl..

[5]  R. Palaniappan,et al.  Classification of Homomorphic Segmented Phonocardiogram Signals Using Grow and Learn Network , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

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

[7]  George P. Petropoulos,et al.  Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery , 2012, Comput. Geosci..

[8]  Nigel Collier,et al.  Bio-Medical Entity Extraction using Support Vector Machines , 2005, Artif. Intell. Medicine.

[9]  Shengyong Wang,et al.  Matrix decomposition based feature extraction for murmur classification. , 2012, Medical engineering & physics.

[10]  Raimo Sepponen,et al.  Atrial septal defect: a diagnostic approach , 2006, Medical and Biological Engineering and Computing.

[11]  Jianpei Zhang,et al.  A Parallel Multi-Class Classification Support Vector Machine Based on Sequential Minimal Optimization , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[12]  Sepideh Babaei,et al.  Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals , 2009, Comput. Biol. Medicine.

[13]  A. Bouakaz,et al.  Selection of a suitable mother wavelet for microemboli classification using SVM and RF signals , 2012, 2012 24th International Conference on Microelectronics (ICM).

[14]  Naif Alajlan,et al.  A wavelet optimization approach for ECG signal classification , 2012, Biomed. Signal Process. Control..

[15]  Zhongwei Jiang,et al.  Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique , 2010, Comput. Biol. Medicine.