Why the study?
Increasing complexity of ECG data and the need for real-time, accurate diagnosis necessitated exploring more robust deep learning architectures such as transformers.
Transformer-based deep learning architectures represent a robust and advanced approach for accurate and real-time ECG interpretation.
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Transformer ECG models extend AI research capabilities; leaves open prospective validation before clinical cardiovascular detection use.
The emergence of deep learning has significantly enhanced the analysis of electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart health. Despite the complexity of ECG interpretation, advanced deep learning models outperform traditional methods. However, the increasing complexity of ECG data and the need for real-time and accurate diagnosis necessitate exploring more robust architectures, such as transformers. Here, we present an in-depth review of transformer architectures that are applied to ECG classification. Originally developed for natural language processing, these models capture complex temporal relationships in ECG signals that other models might overlook. We conducted an extensive search of the latest transformer-based models and summarize them to discuss the advances and challenges in their application and suggest potential future improvements. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation.
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Zibin Zhao (2023) studied this question.
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