Why the study?
Visual assessment of dynamic, complex ECG signals is time-consuming and difficult, creating a need for an automated system to assist in arrhythmia detection.
Does a deep learning model accurately classify ECG signals into cardiac arrhythmia, congestive heart failure, and normal sinus rhythm?
Population
ECG data from the MIT-BIH and BIDMC databases available on PhysioNet
Comparison
Fine-tuned ResNet 50 and AlexNet models for ECG classification
Design
Algorithm development and validation study
Authors
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Supports DL ECG classification development; leaves open prospective validation before clinical adoption.
Does a deep learning model accurately classify ECG signals into cardiac arrhythmia, congestive heart failure, and normal sinus rhythm?
A deep learning model using fine-tuned ResNet 50 and AlexNet achieved 99.2% accuracy in classifying ECG signals into arrhythmia, heart failure, and normal rhythm.
Daydulo et al. (2023) studied this question.
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