A survey of transformers and large language models for ECG diagnosis: advances, challenges, and future directions

A survey of transformers and large language models for ECG diagnosis: advances, challenges, and future directions

<p dir="ltr">Electrocardiograms (ECGs) are widely utilized in clinical practice as a non-invasive diagnostic tool for detecting cardiovascular diseases. Convolutional neural networks (CNNs) have been the primary choice for ECG analysis due to their capability to process raw signals....

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Bibliographic Details
Main Author: Mohammed Yusuf Ansari (16904523) (author)
Other Authors: Mohammed Yaqoob (344668) (author), Mohammed Ishaq (22302736) (author), Eduardo Feo Flushing (22503080) (author), Iffa Afsa changaai Mangalote (22503083) (author), Sarada Prasad Dakua (14151789) (author), Omar Aboumarzouk (18427923) (author), Raffaella Righetti (17585967) (author), Marwa Qaraqe (10135172) (author)
Published: 2025
Subjects:
Engineering
Biomedical engineering
Health sciences
Health services and systems
Information and computing sciences
Artificial intelligence
Arrhythmia
ECG representation
Hallucination
Large language models
Myocardial infarction
Positional encoding
Self-attention architecture
Single-beat and multi-beat analysis
Sleep apnea
Zero-shot diagnosis
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