Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders

The electrocardiogram (ECG) is an efficient and noninvasive indicator for arrhythmia detection and prevention. In real-world scenarios, ECG signals are prone to be contaminated with various noises, which may lead to wrong interpretation. Therefore, significant attention has been paid on denoising of...

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Veröffentlicht in:	IEEE access Jg. 7; S. 60806 - 60813
Hauptverfasser:	Chiang, Hsin-Tien, Hsieh, Yi-Yen, Fu, Szu-Wei, Hung, Kuo-Hsuan, Tsao, Yu, Chien, Shao-Yi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Arrhythmia Artificial neural networks Cardiac arrhythmia Cardiac stress tests Convolution Decoding denoising autoencoders Electrocardiography fully convolutional network Neural networks Noise Noise measurement Noise reduction Performance evaluation Root-mean-square errors signal denoising Signal to noise ratio
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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