Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder

Wearable technology enables the unsupervised recording of electrocardiogram (ECG) signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical approaches and explainability. This work investigates the feasibility of medically explainable anomaly detection through disenta...

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Bibliographic Details
Published in:	Computers in biology and medicine Vol. 184; p. 109422
Main Authors:	Kapsecker, Maximilian, Möller, Matthias C., Jonas, Stephan M.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.01.2025 Elsevier Limited
Subjects:	Anomalies Datasets Dimensional analysis EKG Electrocardiography Electrocardiography - methods Embedding Explainable anomaly detection Heart Humans Internal Medicine Learning Myocardial infarction Other Parameterization Personalization Representational learning Signal Processing, Computer-Assisted Ventricle Wearable technology Representational learning Electrocardiography Personalization Explainable anomaly detection
ISSN:	0010-4825, 1879-0534, 1879-0534
Online Access:	Get full text
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