JOURNAL ARTICLE

Abnormal Heart Sound Detection by Using Temporal Convolutional Network

Keqi LiuLei YuanHuang Cheng-jiWenyuan WuQiangwei WangGang Wu

Year: 2022 Journal:   2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) Pages: 1026-1029

Abstract

Abnormal heart sound detection is great of significance because of the frequent occurrence of heart diseases. However, the automatic diagnosis for abnormal heart sound has a high requirement for domain knowledge and the signal noise poses an increased difficulty of diagnosis. In this paper, we propose a temporal convolutional network (TCN) to automatically detect abnormal heart sounds. Specifically, a noise removing technology is applied to original signals. Then, a TCN architecture is carefully designed to adapt the properties of heartbeat sound. The proposed method is tested on the Physionet dataset, and the results show our method contains potential ability in abnormal heart sound detection.

Keywords:
Heartbeat Computer science Sound (geography) Noise (video) Heart sounds Speech recognition Artificial intelligence Convolutional neural network Bioacoustics Pattern recognition (psychology) Frequency domain Domain (mathematical analysis) Computer vision Acoustics Telecommunications Medicine Cardiology Mathematics

Metrics

5
Cited By
4.77
FWCI (Field Weighted Citation Impact)
15
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Phonocardiography and Auscultation Techniques
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
Music and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
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