Heart Sounds Classification using Adaptive Wavelet Threshold and 1D LDCNN

Jianqiang Hu1, 3, Qingli Hu2 and Mingfeng Liang1, 3

  1. School of Computer and Information Engineering, Xiamen University of Technology
    361024 Xiamen, P. R. China
  2. iFlytek Research, iFlytek Co. Ltd.
    230088 Hefei, P.R. China
  3. Key Laboratory of Internet-of-Things Applications of Fujian Province
    Xiamen University of Technology, 361024 Xiamen, P.R. China


Heart sounds classification plays an important role in cardiovascular disease detection. Currently, deep learning methods for heart sound classification with heavy parameters consumption cannot be deployed in environments with limited memory and computational budgets. Besides, de-noising of heart sound signals (HSSs) can affect accuracy of heart sound classification, because erroneous removal of meaningful components may lead to heart sound distortion. In this paper, an automated heart sound classification method using adaptive wavelet threshold and 1D LDCNN (One-dimensional Lightweight Deep Convolutional Neural Network) is proposed. In this method, we exploit WT (Wavelet Transform) with an adaptive threshold to de-noise heart sound signals (HSSs). Furthermore, we utilize 1D LDCNN to realize automatic feature extraction and classification for de-noised heart sounds. Experiments on PhysioNet/CinC 2016 show that our proposed method achieves the superior classification results and excels in consumption of parameter comparing to state-of-the-art methods.

Key words

heart sounds classification, adaptive wavelet threshold, lightweight deep convolutional neural network

Digital Object Identifier (DOI)


Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Hu, J., Hu, Q., Liang, M.: Heart Sounds Classification using Adaptive Wavelet Threshold and 1D LDCNN. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS230418059H