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심전도 신호의 전력선 잡음 제거를 위한 Deep De-noising Network 설계

Other Titles
 Design of Deep De-nosing Network for Power Line Artifact in Electrocardiogram 
Authors
 권오윤  ;  이지은  ;  권준환  ;  임성준  ;  유선국 
Citation
 Journal of Korea Multimedia Society (멀티미디어학회논문지), Vol.23(3) : 402-411, 2020-03 
Journal Title
 Journal of Korea Multimedia Society (멀티미디어학회논문지) 
ISSN
 1229-7771 
Issue Date
2020-03
Keywords
Deep Neural Network ; Electrocardiogram ; De-noising ; Power Line Artifact
Abstract
Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.
Files in This Item:
T202001861.pdf Download
DOI
10.9717/kmms.2020.23.3.402
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179064
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