A Novel Feature Vector for ECG Classification using Deep Learning
IntelITSIS 2023, pp. 227-238
Editorial summary
Measured ECG wave durations and amplitudes form an interpretable feature vector for a neural-network classifier. Computational experiments compare classification using these cardiac-cycle measurements with methods that process the full signal. The aim is to make the model’s inputs understandable to clinicians.
Bibliographic reference
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Oleksii Kovalchuk, Pavlo Radiuk, Olexander Barmak, Sergii Petrovskyi, Iurii Krak. "A Novel Feature Vector for ECG Classification using Deep Learning". IntelITSIS 2023, pp. 227-238, 2023.
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