Why the study?
Deep learning models for ECG heartbeat classification require substantial training data and have limited performance on imbalanced datasets, often yielding zero accuracy for minority classes.
Does transfer learning improve heartbeat classification accuracy in imbalanced ECG datasets?
Does transfer learning improve heartbeat classification accuracy in imbalanced ECG datasets?
Transfer learning significantly improves the accuracy of deep learning models for heartbeat classification in imbalanced ECG datasets, particularly for minority classes.
May improve minority-class detection in ECG arrhythmia classification; leaves open prospective clinical validation.
Electrocardiogram (ECG) data recorded by medical devices are hard to analyze manually. Therefore, it is important to analyze and categorize each heartbeat using machine learning. Recently, advancements in machine learning have made classification of complex data easy and fast. However, these machine learning algorithms require sufficient amount of training data and have limited performance in case the data is imbalance. In case of MIT-BIH arrhythmia dataset, the distribution of training instances are quite imbalance. Many machine learning, particularly deep learning, algorithms give high accuracy on these datasets but still the minority classes have zero accuracy. In this paper, we improve the accuracy of minority classes without hurting the overall accuracy of other classes using transfer learning. The accuracy of existing deep learning model is increased from 90.67% to 98.47%, respectively.
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Sabir et al. (2022) studied this question.
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