Heart Sounds Classification using Adaptive Wavelet Threshold and 1D LDCNN
- School of Computer and Information Engineering, Xiamen University of Technology
361024 Xiamen, P. R. China
hujianqiang@tsinghua.org.cn - iFlytek Research, iFlytek Co. Ltd.
230088 Hefei, P.R. China
huqiangli2014@outlook.com - Key Laboratory of Internet-of-Things Applications of Fujian Province
Xiamen University of Technology, 361024 Xiamen, P.R. China
lmfanny115@hotmail.com
Abstract
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)
https://doi.org/10.2298/CSIS230418059H
Publication information
Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
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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