Convolutional Autoencoder With Sequential and Channel Attention for Robust ECG Signal Denoising With Edge Device Implementation

Electrocardiograms (ECG) are vital for diagnosing various cardiac conditions but are often corrupted by noise from multiple sources, which can hinder accurate interpretation. Denoising ECG signals is particularly challenging because noise usually overlaps with the frequency range of the signal of in...

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Vydané v:	IEEE access Ročník 13; s. 54407 - 54422
Hlavní autori:	Wicaksana Ramadhan, Alif, Kushirayati, Syifa, Aurellia, Salsabila, Luthfi Ramadhan, Mgs M., Hannan Hunafa, Muhammad, Febrian Rachmadi, Muhammad, Jasa Mantau, Aprinaldi, Nurmaini, Siti, Mandala, Satria, Jatmiko, Wisnu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autoencoder Autoencoders Convolution Convolutional neural networks convolutional neural networks (CNN) Decoding ECG Electrocardiography Floating point arithmetic Frequency ranges Heart Long short term memory LSTM Monitoring Noise reduction signal denoising
ISSN:	2169-3536, 2169-3536
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