A Novel Method for Detecting Noise Segments in ECG Signals

被引：1

作者：

Zhu, Wenliang ^{[1
]}

Ma, Gang ^{[1
]}

Chen, Yuhang ^{[1
]}

Qiu, Lishen ^{[1
]}

Zheng, Lesong ^{[2
]}

Wang, Lirong ^{[2
]}

机构：

[1] Univ Sci & Technol China, Suzhou Inst Biomed Engn & Technol, Suzhou, Peoples R China

[2] Soochow Univ, Sch Elect & Informat Engn, Suzhou, Peoples R China

来源：

2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021) | 2021年

关键词：

wearable; electrocardiogram; deep learning; muscle artifact; electrode motion artifact; QRS DETECTION; QUALITY INDEXES; ELECTROCARDIOGRAM;

D O I：

10.1109/TrustCom53373.2021.00228

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Wearable electrocardiogram (ECG) monitoring systems were effective ways to diagnose intermittent cardio diseases. However, the Electrode Motion Artifact (EMA) and the Muscle Artifact (MA) destroy the incipient shape of ECG signals, decrease the accuracy of diagnostic results. Here, we proposed a novel method for detecting destroyed segments of ECG signals. The method was based on a deep learning network, and its performance was evaluated on a synthetic dataset of the MIT-BIH arrhythmia database. Its practicability was tested with three R-peak detection algorithms. By removing the destroyed segments in ECG signals, the sensitise and positive prediction of these R-peak detection algorithms were promoted significantly.

引用

页码：1570 / 1574

页数：5

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