Transforming ECG Diagnosis:An In-depth Review of Transformer-based
  DeepLearning Models in Cardiovascular Disease Detection
Zibin Zhao
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.