Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications.

[1]  Saurabh Prasad,et al.  Class-Dependent Sparse Representation Classifier for Robust Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Yineng Zheng,et al.  A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification , 2015, Expert Syst. Appl..

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

[4]  Masun Nabhan Homsi,et al.  Ensemble methods with outliers for phonocardiogram classification , 2017, Physiological measurement.

[5]  U. Rajendra Acharya,et al.  A new approach for arrhythmia classification using deep coded features and LSTM networks , 2019, Comput. Methods Programs Biomed..

[6]  R. Scully,et al.  AGING CHANGES IN THE AORTIC AND MITRAL VALVES. HISTOLOGIC AND HISTOCHEMICAL STUDIES, WITH OBSERVATIONS ON THE PATHOGENESIS OF CALCIFIC AORTIC STENOSIS AND CALCIFICATION OF THE MITRAL ANNULUS. , 1965, The American journal of pathology.

[7]  Ketty Savino,et al.  Handheld Ultrasound and Focused Cardiovascular Echography: Use and Information , 2019, Medicina.

[8]  K. I. Ramachandran,et al.  A novel heart sound activity detection framework for automated heart sound analysis , 2014, Biomed. Signal Process. Control..

[9]  U. RajendraAcharya.  Advances in cardiac signal processing , 2007 .

[10]  Euripidis Loukis,et al.  Support Vectors Machine-based identification of heart valve diseases using heart sounds , 2009, Comput. Methods Programs Biomed..

[11]  Giacomo Capizzi,et al.  Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks , 2018, Biomedical engineering letters.

[12]  Shyamala C. Doraisamy,et al.  Multi-level basis selection of wavelet packet decomposition tree for heart sound classification , 2013, Comput. Biol. Medicine.

[13]  Björn Eskofier,et al.  Generic performance measure for multiclass-classifiers , 2017, Pattern Recognit..

[14]  K. P. Soman,et al.  Robust heart sound activity detection in noisy environments , 2010 .

[15]  K. Yutzey,et al.  Heart Valve Development: Regulatory Networks in Development and Disease , 2009, Circulation research.

[16]  Li Ke,et al.  Classification of Heart Sounds Based on the Wavelet Fractal and Twin Support Vector Machine , 2019, Entropy.

[17]  Chandan Chakraborty,et al.  Application of Higher Order cumulant Features for Cardiac Health Diagnosis using ECG signals , 2013, Int. J. Neural Syst..

[18]  Raymond L. Watrous,et al.  Computer-Aided Auscultation of the Heart: From Anatomy and Physiology to Diagnostic Decision Support , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[20]  W. M. Zhang,et al.  Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation , 2011, IEEE Transactions on Instrumentation and Measurement.

[21]  Vivek Nigam,et al.  Accessing heart dynamics to estimate durations of heart sounds , 2005, Physiological measurement.

[22]  S. M. Debbal,et al.  Computerized heart sounds analysis , 2008, Comput. Biol. Medicine.

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

[24]  D. Mozaffarian,et al.  Executive Summary: Heart Disease and Stroke Statistics—2015 Update A Report From the American Heart Association , 2011, Circulation.

[25]  F. Cáceres-Lóriga,et al.  Prosthetic heart valve thrombosis: pathogenesis, diagnosis and management. , 2006, International journal of cardiology.

[26]  Lionel Tarassenko,et al.  Support vector machine hidden semi-Markov model-based heart sound segmentation , 2014, Computing in Cardiology 2014.

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

[28]  Simon Haykin,et al.  The chirplet transform: physical considerations , 1995, IEEE Trans. Signal Process..

[29]  Soonil Kwon,et al.  Classification of Heart Sound Signal Using Multiple Features , 2018, Applied Sciences.

[30]  U. Rajendra Acharya,et al.  An efficient compression of ECG signals using deep convolutional autoencoders , 2018, Cognitive Systems Research.

[31]  M J Legato,et al.  Gender and the heart: sex-specific differences in normal anatomy and physiology. , 2000, The journal of gender-specific medicine : JGSM : the official journal of the Partnership for Women's Health at Columbia.

[32]  Algirdas Maknickas,et al.  Recognition of normal–abnormal phonocardiographic signals using deep convolutional neural networks and mel-frequency spectral coefficients , 2017, Physiological measurement.

[33]  Yu Tsao,et al.  S1 and S2 Heart Sound Recognition Using Deep Neural Networks , 2017, IEEE Trans. Biomed. Eng..

[34]  Lertlak Chaothawee,et al.  Diagnostic approach to assessment of valvular heart disease using MRI—Part I: a practical approach for valvular regurgitation , 2012, Heart Asia.

[35]  Dhanjoo N. Ghista,et al.  INFARCTED LEFT VENTRICLE CLASSIFICATION FROM CROSS-SECTIONAL ECHOCARDIOGRAMS USING RELATIVE WAVELET ENERGY AND ENTROPY FEATURES , 2016 .

[36]  Juan Ignacio Godino-Llorente,et al.  Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals , 2009, Annals of Biomedical Engineering.

[37]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[38]  Christian Brandt,et al.  A robust heart sounds segmentation module based on S-transform , 2013, Biomed. Signal Process. Control..

[39]  E. Rapaport.  Natural history of aortic and mitral valve disease. , 1975, The American journal of cardiology.

[40]  U. Rajendra Acharya,et al.  MMSFL-OWFB: A novel class of orthogonal wavelet filters for epileptic seizure detection , 2018, Knowl. Based Syst..

[41]  Ricardo Fraiman,et al.  An anova test for functional data , 2004, Comput. Stat. Data Anal..

[42]  Ram Bilas Pachori,et al.  Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features , 2019, IEEE Sensors Letters.

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

[44]  Syed Anas Imtiaz,et al.  Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review , 2019, IEEE Access.

[45]  David G. Stork,et al.  Pattern Classification , 1973 .

[46]  D. L. Johnston,et al.  Evaluation of cardiac valvular disease with MR imaging: qualitative and quantitative techniques. , 2003, Radiographics : a review publication of the Radiological Society of North America, Inc.

[47]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[48]  Anjan Gudigar,et al.  A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images , 2019, Journal of Medical Systems.

[49]  Ram Bilas Pachori,et al.  Classification of cardiac sound signals using constrained tunable-Q wavelet transform , 2014, Expert Syst. Appl..

[50]  Roberto Rosas-Romero,et al.  Forecasting of stock return prices with sparse representation of financial time series over redundant dictionaries , 2016, Expert Syst. Appl..

[51]  Sukryool Kang,et al.  Automated Identification of Innocent Still's Murmur in Children , 2017, IEEE Transactions on Biomedical Engineering.

[52]  Ali Ghaffari,et al.  Home Search Collections Journals About Contact us My IOPscience Detection of pathological heart sounds , 2017 .

[53]  Ganesh R. Naik,et al.  Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme , 2019, Comput. Methods Programs Biomed..

[54]  Zhongwei Jiang,et al.  Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system , 2014, Expert Syst. Appl..

[55]  Miguel Tavares Coimbra,et al.  Heart sound segmentation of pediatric auscultations using wavelet analysis , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[56]  R. Bonow,et al.  Valvular heart disease: diagnosis and management. , 2010, Mayo Clinic proceedings.