Key result
Patient-adaptive beat-wise temporal transformer model achieves ~99% accuracy for AF classification.
Why the study?
Traditional deep learning models for AF classification often focus on generalized patterns, potentially overlooking patient-specific differences and temporal variations in ECG morphology during long-term cardiac monitoring.
Does a patient-adaptive beat-wise temporal transformer model accurately classify atrial fibrillation in continuous long-term cardiac monitoring?
Does a patient-adaptive beat-wise temporal transformer model accurately classify atrial fibrillation in continuous long-term cardiac monitoring?
A novel patient-adaptive beat-wise temporal transformer model demonstrated high accuracy and F1 scores for atrial fibrillation classification using continuous long-term cardiac monitoring data.
Promising for adaptive AF detection in patch monitoring; leaves open prospective validation before clinical use.
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires accurate diagnosis and management, especially in long-term cardiac monitoring (LTCM) scenarios. Although ECG signal morphology can vary between patients and over time, traditional deep learning models often focus on generalized classification, potentially overlooking patient-specific differences. In this study, we developed a patient-adaptive beat-wise temporal transformer model aimed at enhancing AF classification performance using data from LTCM devices. The model introduces a symbolic token representing the ideal ECG morphology of a patient, enabling the transformer to effectively reference patient-specific information and capture temporal variations in ECG morphology. To evaluate the model’s classification performance, we trained it on public databases and tested it on patch datasets. The model achieved an accuracy of 0.987, precision of 0.965, sensitivity of 0.979, specificity of 0.99, and an F1 score of 0.982 on the Patch A dataset. On the Patch B dataset, it attained an accuracy of 0.953, precision of 0.91, sensitivity of 0.971, specificity of 0.942, and an F1 score of 0.951. We anticipate that the proposed approach will be particularly beneficial for monitoring AF in real-world clinical settings, especially in LTCM scenarios.
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Kim et al. (2024) studied Atrial fibrillation. Patient-adaptive beat-wise temporal transformer model was evaluated on AF classification performance (accuracy, precision, sensitivity, specificity, F1 score). A patient-adaptive beat-wise temporal transformer model achieved high atrial fibrillation classification performance, with an accuracy of 0.987 and F1 score of 0.982 on the Patch A dataset.
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