State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

Petmezas, Georgios; Stefanopoulos, Leandros; Kilintzis, Vassilis; Tzavelis, Andreas; Rogers, John A; Katsaggelos, Aggelos K; Maglaveras, Nicos

Petmezas, Georgios; Stefanopoulos, Leandros; Kilintzis, Vassilis; Tzavelis, Andreas; Rogers, John A; Katsaggelos, Aggelos K; Maglaveras, Nicos.

Afiliación

Petmezas G; Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Stefanopoulos L; Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Kilintzis V; Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Tzavelis A; Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States.
Rogers JA; Department of Material Science, Northwestern University, Evanston, IL, United States.
Katsaggelos AK; Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States.
Maglaveras N; Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.

JMIR Med Inform ; 10(8): e38454, 2022 Aug 15.

Article en En | MEDLINE | ID: mdl-35969441

Palabras clave

CNN; ECG; ECG databases; LSTM; ResNet; clinical decision; convolutional neural networks; decision support; deep learning; diagnostic tools; electrocardiogram; long short-term memory; residual neural network

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Systematic_reviews Aspecto: Patient_preference Idioma: En Revista: JMIR Med Inform Año: 2022 Tipo del documento: Article País de afiliación: Grecia Pais de publicación: Canadá

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