A convolutional autoencoder framework for ECG signal analysis

A convolutional autoencoder framework for ECG signal analysis

Electrocardiographic (ECG) signals are used to evaluate heart activity and to identify disease-related anomalies. Reliable support systems are useful for analyzing ECG signals, for instance, in long-term data acquisition and evaluation (e.g., 24-hour holter recording) or to support physicians in rea...

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Bibliographic Details
Main Authors: Ugo Lomoio, Patrizia Vizza, Raffaele Giancotti, Salvatore Petrolo, Sergio Flesca, Fabiola Boccuto, Pietro Hiram Guzzi, Pierangelo Veltri, Giuseppe Tradigo
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Heliyon
Subjects:
ECG
Anomaly detection
Signal annotation
Autoencoder
Decision support systems
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024175482
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