Why the study?
Traditional ECG classification methods rely on complex signal processing phases that lead to expensive designs.
Does a 1-D convolutional deep residual neural network with SMOTE improve the classification accuracy of ECG heartbeats?
Population
ECG signals from the PhysioNet MIT-BIH Arrhythmia database
Comparison
1-D convolutional deep ResNet with SMOTE vs other 1-D CNNs
Design
Algorithm development and ten-fold cross validation study
Key result
The proposed 1-D convolutional deep residual neural network with SMOTE achieved an average accuracy of 98.63% for the classification of five heartbeat types.
Authors
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May support automated ECG analysis; hypothesis-generating pending external clinical validation.
Does a 1-D convolutional deep residual neural network with SMOTE improve the classification accuracy of ECG heartbeats?
Absolute Event Rate: 98.63% vs 95%
A 1-D convolutional deep residual neural network combined with SMOTE for data balancing achieves high accuracy (98.63%) in classifying five types of ECG heartbeats.
Khan et al. (2023) studied Arrhythmia (n=47). 1-D convolutional deep residual neural network (ResNet) with SMOTE vs. Model without SMOTE was evaluated on Classification accuracy. The proposed 1-D convolutional deep residual neural network with SMOTE achieved an average accuracy of 98.63% for the classification of five heartbeat types.
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