Key result
Deep learning and hybrid features yield the best performance for categorizing cardiac diseases using ECGs.
Why the study?
Increasing numbers of heart patients have created a need to develop automatic detection and classification techniques for arrhythmias and cardiac abnormalities to alleviate physician workload.
Design
Review
Authors
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Supports DL-hybrid ECG classification; hypothesis-generating and should not change practice without prospective validation.
This review highlights that deep learning methods and hybrid feature extraction provide the best performance for automated ECG signal classification and cardiac disease categorization.
Mishra et al. (2024) conducted a review in Cardiac Diseases. ECG Signal Processing and Analysis Techniques was evaluated. Deep learning methods and hybrid features provide the best performance for categorizing cardiac diseases using ECG signals.
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