Electrocardiogram lead conversion from single-lead blindly-segmented signals

Background The standard configuration’s set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient’s limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can recons...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 22; H. 1; S. 314 - 15
Hauptverfasser:	Beco, Sofia C., Pinto, João Ribeiro, Cardoso, Jaime S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 29.11.2022 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Adaptation Autoencoder BMC Supplements Reviewed Coders Configurations Conversion Databases, Factual Deep learning EKG Electrocardiogram Electrocardiogram (ECG) Electrocardiography Electrodes Encoders-Decoders Feasibility studies Health Informatics Heart Heart Diseases Humans Information Systems and Communication Service Leads Machine learning Management of Computing and Information Systems Medical diagnosis Medicine Medicine & Public Health Methodology Methods Neural networks Regression analysis Signal processing U-Net Wearable computers Wearable Electronic Devices Wearable technology Portugal Deep learning Leads Electrocardiogram (ECG) Autoencoder Conversion U-Net
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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