Why the study?
Available datasets to train and evaluate ECG arrhythmia machine learning models are often very small, and well-annotated public datasets are lacking.
Demonstrates that deep transfer learning can classify arrhythmias on small datasets and highlights the critical need to follow AAMI EC57 standards to prevent data leakage in machine learning models.
Small ECG datasets constrain deep learning arrhythmia classifiers; leaves open need for larger annotated repositories before reliable clinical use.
Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related applications and datasets, many arrhythmia classifiers using deep learning methods have been proposed in recent years. However, sizes of the available datasets from which to build and assess machine learning models is often very small and the lack of well-annotated public ECG datasets is evident. In this paper, we propose a deep transfer learning framework that is aimed to perform classification on a small size training dataset. The proposed method is to fine-tune a general-purpose image classifier ResNet-18 with MIT-BIH arrhythmia dataset in accordance with the AAMI EC57 standard. This paper further investigates many existing deep learning models that have failed to avoid data leakage against AAMI recommendations. We compare how different data split methods impact the model performance. This comparison study implies that future work in arrhythmia classification should follow the AAMI EC57 standard when using any including MIT-BIH arrhythmia dataset.
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Cao et al. (2022) studied this question.
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