Review Article
Open Access

Research process on deep learning methods for heart sounds classification

Weifeng Wu
Weifeng Wu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Yongqian Zhang
Yongqian Zhang
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Qianfeng Xu
Qianfeng Xu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Jiuzhou Zhao
Jiuzhou Zhao
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Rongguo Yan
Rongguo Yan
yanrongguo@usst.edu.cn
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Address correspondence to
Article notes
Highlights
Rongguo Yan, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. Email: yanrongguo@usst.edu.cn.
Received February 7, 2023; Accepted August 25, 2023; Published September 30, 2023


  • Denoising, segmentation, and feature extraction of heart sounds as well as its classification process are reviewed.

  • A detailed exposition of diverse deep learning methods for heart sounds classification is presented.

Review Article
Open Access
Research process on deep learning methods for heart sounds classification
Weifeng Wu
Weifeng Wu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Yongqian Zhang
Yongqian Zhang
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Qianfeng Xu
Qianfeng Xu
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Jiuzhou Zhao
Jiuzhou Zhao
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
,
Rongguo Yan
Rongguo Yan
yanrongguo@usst.edu.cn
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Address correspondence to
Rongguo Yan, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. Email: yanrongguo@usst.edu.cn.
Article notes
Received February 7, 2023; Accepted August 25, 2023; Published September 30, 2023


Highlights
  • Denoising, segmentation, and feature extraction of heart sounds as well as its classification process are reviewed.

  • A detailed exposition of diverse deep learning methods for heart sounds classification is presented.

2023 Sept;1(2):55-64
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Abstract

Cardiovascular diseases are still the primary threats to people's health around the world. Automatic heart sound classification technology, as a fast and efficient means for diagnosis and treatment, is of great clinical significance. With the rapid development of artificial intelligence technology, deep learning algorithms are widely used in automatic heart sound classification. This paper reviewed the key technologies related to the automatic classification of heart sounds in recent years, including heart sound denoising, segmentation, feature extraction, and classification recognition. The classification and recognition technologies related to deep learning are presented in detail, with a focus on the application and development of convolutional neural network and recurrent neural network, as well as various combination models for heart sound classification in the past five years.

Keywords: Cardiovascular disease, deep learning, heart sounds classification, convolutional neural network, recurrent neural network
Progress in Medical Devices

ISSN: 2957-5478

Volume 1, Issue 2

September 2023

Pages: 55-130

PDF CITE Accesses: 7
Progress in Medical Devices
ISSN: 2957-5478
ZENTIME PUBLISHING CORPORATION LIMITED
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