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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Veröffentlicht in:	IEEE access Jg. 13; S. 54407 - 54422
Hauptverfasser:	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
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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