Investigating the Robustness of Deep Learning to Electrocardiogram Noise

被引：0
|
作者：
Venton, Jenny [1 ]
Aston, Philip J. [1 ,2 ]
机构：
[1] Natl Phys Lab, Dept Data Sci, Hampton Rd, Teddington TW11 0LW, Middx, England
[2] Univ Surrey, Dept Math, Guildford, Surrey, England
来源：
2021 COMPUTING IN CARDIOLOGY (CINC) | 2021年
关键词：
D O I：
10.22489/CinC.2021.101
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Deep learning models for electrocardiogram (ECG) classification can be affected by the presence of physiological noise on the ECG, as shown in previous work. In this study, we explore the impact of different physiological noise types, and differing signal-to-noise ratios (SNRs) of noise on classification performance. We find that classification performance is impacted differently by different noise types. In addition, the best classification performance comes from using a network trained on clean ECGs to classify clean ECGs. In conclusion, this study has revealed several questions regarding inclusion or exclusion of noise on the ECG for training and classification by deep learning models.
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页数：4
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