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Heart Sound Classification Using Deep Learning Techniques Based on Log-mel Spectrogram

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Abstract

In this study, two models for classifying heart rate sounds are proposed to classify heart sound by deep learning techniques based on the log-mel spectrogram of heart sound signals. The heart sound dataset comprises five classes, one normal class and four anomalous classes, namely, Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, and Murmur in systole. First, the heart sound signals are framed to a consistent length and thereafter extract the log-mel spectrogram features. Two deep learning models, long short-term memory and convolution neural network are proposed to classify heartbeat sounds based on the extracted features. Analysis results demonstrated the high performance of classification models, with an overall accuracy of about 99.67%. The results also showed higher performance compared to previous studies.

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Data Availability

The data that support the findings of this study are available from “Classification-of-Heart-Sound-Signal-Using-Multiple-Features.” https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/find/master. The processed data and program are available in https://github.com/tuanktcs/Heart-sound-classification.

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Hung Yen University of Technology and Education, Hung Yen, Vietnam

    Minh Tuan Nguyen

  2. Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan, ROC

    Wei Wen Lin

  3. Department of Life Science, Tunghai University, Taichung, Taiwan, ROC

    Wei Wen Lin

  4. Department of Mechanical and Computer-Aided Engineering, Feng Chia University, Taichung, Taiwan, ROC

    Jin H. Huang

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  1. Minh Tuan Nguyen
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  2. Wei Wen Lin
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Nguyen, M.T., Lin, W.W. & Huang, J.H. Heart Sound Classification Using Deep Learning Techniques Based on Log-mel Spectrogram. Circuits Syst Signal Process 42, 344–360 (2023). https://doi.org/10.1007/s00034-022-02124-1

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  • DOI: https://doi.org/10.1007/s00034-022-02124-1

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