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http://dx.doi.org/10.9717/kmms.2020.23.3.402

Design of Deep De-nosing Network for Power Line Artifact in Electrocardiogram  

Kwon, Oyun (Graduate Program of Medical Device Industry, Yonsei University)
Lee, JeeEun (Graduate Program of Biomedical Engineering, Yonsei University)
Kwon, Jun Hwan (Graduate Program of Biomedical Engineering, Yonsei University)
Lim, Seong Jun (Graduate Program of Medical Device Industry, Yonsei University)
Yoo, Sun Kook (Dept. of Medical Engineering, Yonsei University College of Medicine)
Publication Information
Journal of Korea Multimedia Society / v.23, no.3, 2020 , pp. 402-411 More about this Journal
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.
Keywords
Deep Neural Network; Electrocardiogram; De-noising; Power Line Artifact;
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