Classification of normal and abnormal heart sounds

Heart hemodynamic status and detection of a cardiovascular disease can be evaluated by analyzing and visualizing the heart waveform through graphs called the Phonocardiogram (PCG). The normal sounds of the heart generate signals that are in the audible frequency range of the human ear. Due to the significance of cardiac auscultation for recognizing pathological cardiac status, there has been special interest in automating the classification of heart sounds in the past years. The objective of this research is to present an automatic classification algorithm for anomaly (normal vs. abnormal heart status) of PCG recordings. For this purpose, distinctive time and frequency features are extracted out of heart sound signals to build a learning model using random forest. The accuracy of the proposed algorithm is about 12% better than state of the art.

[1]  H. Liang,et al.  A feature extraction algorithm based on wavelet packet decomposition for heart sound signals , 1998, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis (Cat. No.98TH8380).

[2]  Ridvan Saraçoglu,et al.  Hidden Markov model-based classification of heart valve disease with PCA for dimension reduction , 2012, Eng. Appl. Artif. Intell..

[3]  Shadnaz Asgari,et al.  Pediatric heart sound segmentation using Hidden Markov Model , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Pedro Gómez Vilda,et al.  Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors , 2004, IEEE Transactions on Biomedical Engineering.

[5]  G. Castellanos-Dominguez,et al.  Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals , 2009, Annals of Biomedical Engineering.

[6]  S. Pavlopoulos,et al.  A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds , 2004, Biomedical engineering online.

[7]  Raimo Sepponen,et al.  Detection of cardiac pathology: time intervals and spectral analysis , 2007, Acta paediatrica.

[8]  Jacques P. de Vos,et al.  Automated Pediatric Cardiac Auscultation , 2007, IEEE Transactions on Biomedical Engineering.

[9]  Bryan R. Conroy,et al.  Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds , 2016, 2016 Computing in Cardiology Conference (CinC).

[10]  Johannes J. Struijk,et al.  No evidence of nonlinear or chaotic behavior of cardiovascular murmurs , 2011, Biomed. Signal Process. Control..

[11]  J J Struijk,et al.  Segmentation of heart sound recordings by a duration-dependent hidden Markov model , 2010, Physiological measurement.

[12]  J.T.E. McDonnell,et al.  Time-frequency and time-scale techniques for the classification of native and bioprosthetic heart valve sounds , 1998, IEEE Transactions on Biomedical Engineering.

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

[14]  Mario Spagnuolo,et al.  Computer analysis of phonocardiograms , 1963 .

[15]  Lionel Tarassenko,et al.  Logistic Regression-HSMM-Based Heart Sound Segmentation , 2016, IEEE Transactions on Biomedical Engineering.

[16]  Egon Toft,et al.  Acoustic Features for the Identification of Coronary Artery Disease , 2015, IEEE Transactions on Biomedical Engineering.

[17]  Ping Wang,et al.  Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model , 2007, Annals of Biomedical Engineering.