Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

The heart sound signal is one of the signals that reflect the health of the heart. Research on the heart sound signal contributes to the early diagnosis and prevention of cardiovascular diseases. As a commonly used deep learning network, convolutional neural network (CNN) has been widely used in images. In this paper, the method of analyzing heart sound through using CNN has been studied. Firstly, the original data set was preprocessed, and then the heart sounds were segmented on U-net, based on the deep CNN. Finally, the classification of heart sounds was completed through CNN. The data from 2016 PhysioNet/CinC Challenge was utilized for algorithm validation, and the following results were obtained. When the heart sound segmented, the overall accuracy rate was 0.991, the accuracy of the first heart sound was 0.991, the accuracy of the systolic period was 0.996, the accuracy of the second heart sound was 0.996, and the accuracy of the diastolic period was 0.997, and the average accuracy rate was 0.995; While in classification, the accuracy was 0.964, the sensitivity was 0.781, and the specificity was 0.873. These results show that deep learning based on CNN shows good performance in the segmentation and classification of the heart sound signal.

[1]  Inderveer Chana,et al.  Heart sound classification using machine learning and phonocardiogram , 2019, Modern Physics Letters B.

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

[3]  Franz Pernkopf,et al.  Heart Sound Segmentation—An Event Detection Approach Using Deep Recurrent Neural Networks , 2018, IEEE Transactions on Biomedical Engineering.

[4]  Sridha Sridharan,et al.  Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection , 2020, ArXiv.

[5]  Hongying Liu,et al.  Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network , 2019, Applied Sciences.

[6]  Qiao Li,et al.  An open access database for the evaluation of heart sound algorithms , 2016, Physiological measurement.

[7]  Richard Socher,et al.  Improving Generalization Performance by Switching from Adam to SGD , 2017, ArXiv.

[8]  R M Rangayyan,et al.  Phonocardiogram signal analysis: a review. , 1987, Critical reviews in biomedical engineering.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Maryam Imani,et al.  Classification of heart sound signal using curve fitting and fractal dimension , 2018, Biomed. Signal Process. Control..

[11]  Leontios J. Hadjileontiadis,et al.  Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features , 2014, IEEE Journal of Biomedical and Health Informatics.

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

[13]  Raymond L. Watrous,et al.  Detection of the first heart sound using a time-delay neural network , 2002, Computers in Cardiology.

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

[15]  Peter Hult,et al.  Feature Extraction for Systolic Heart Murmur Classification , 2006, Annals of Biomedical Engineering.

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

[17]  Alain Dieterlen,et al.  Localization of Heart Sounds Based on S-Transform and Radial Basis Function Neural Network , 2011 .

[18]  Winston S. Percybrooks,et al.  Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features , 2020, Applied Sciences.

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

[20]  Rangaraj M. Rangayyan,et al.  A Three-Channel Microcomputer System for Segmentation and Characterization of the Phonocardiogram , 1987, IEEE Transactions on Biomedical Engineering.

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  Fengyu Cong,et al.  Classification of Heart Sounds Using Convolutional Neural Network , 2020, Applied Sciences.

[23]  Muhammad Salman Khan,et al.  Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features , 2020, Physiological measurement.

[24]  Rui Pedro Paiva,et al.  Noise detection during heart sound recording using periodicity signatures , 2011, Physiological measurement.

[25]  Eliasz Kantoch,et al.  A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal , 2017, ISAT.

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

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

[28]  P Ask,et al.  A method for accurate localization of the first heart sound and possible applications , 2008, Physiological measurement.

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

[30]  H. Naseri,et al.  Detection and Boundary Identification of Phonocardiogram Sounds Using an Expert Frequency-Energy Based Metric , 2012, Annals of Biomedical Engineering.

[31]  Miguel Tavares Coimbra,et al.  Deep Convolutional Neural Networks for Heart Sound Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.