Why the study?
ECG classification using deep learning has shown promising results, and reliable methods are needed to accurately diagnose and classify cardiac diseases using ECG data.
Comparison
GoogleNet vs AlexNet vs ResNet Deep-CNN models
Key result
The GoogleNet deep convolutional neural network model achieved an accuracy of 96% in classifying ECG signals into normal sinus rhythm, congestive heart failure, and arrhythmia.
Authors
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CNNs classify ECG signals at up to 96% accuracy; leaves open prospective clinical validation before practice change.
Deep learning models, particularly GoogleNet, can accurately classify ECG signals into normal sinus rhythm, arrhythmia, and congestive heart failure using continuous wavelet transform scalograms.
Nadour et al. (2023) studied Arrhythmia and Congestive Heart Failure (n=162). Deep Convolutional Neural Networks (GoogleNet, AlexNet, ResNet) was evaluated on Classification accuracy. The GoogleNet deep convolutional neural network model achieved an accuracy of 96% in classifying ECG signals into normal sinus rhythm, congestive heart failure, and arrhythmia.