A comprehensive review on encoder–decoder architectures in ECG signal compression and denoising: opportunities, challenges, and prospects

Purpose Cardiovascular disease diagnosis relies on the accurate interpretation of electrocardiogram (ECG) signals, which are sensitive to noise and artifacts. Furthermore, with ubiquitous long-term monitoring technology, portable, lightweight wearable devices have limited memory and energy resources...

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Veröffentlicht in:	Research on biomedical engineering Jg. 41; H. 4; S. 57
Hauptverfasser:	Das, Maumita, Sahana, Bikash Chandra
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Cardiovascular diseases Coders Comparative analysis Comparative studies Compression Deep learning EKG Electrocardiography Energy resources Energy sources Engineering Flexibility Fourier transforms Generative adversarial networks Heart Mathematical models Memory devices Neural networks Noise Noise reduction Noise sensitivity Peripheral equipment (computers) Portable equipment Real time Review Signal processing Signal transmission Surveys Telemetry Wavelet transforms Wearable technology Deep learning models Detection Autoencoder Data preprocessing Electrocardiogram Denoising
ISSN:	2446-4732, 2446-4740
Online-Zugang:	Volltext
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