Key result
A deep learning-based ECG signal delineation model accurately identified atrial fibrillation, achieving 98.91% sensitivity, 99.79% specificity, and 99.79% accuracy.
Why the study?
Manual ECG interpretation is experience-dependent and tedious, while existing deep learning methods are limited to specific abnormalities rather than generalizable waveform delineation combined with medical knowledge rules.
Does a deep learning-based short single-lead ECG signal delineation model accurately identify atrial fibrillation?
Does a deep learning-based short single-lead ECG signal delineation model accurately identify atrial fibrillation?
A deep learning-based ECG signal delineation model combined with medical knowledge rules can accurately identify atrial fibrillation from short single-lead ECGs.
Supports automated AF detection on short single-lead ECGs; leaves open prospective validation before clinical use.
Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.
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Tutuko et al. (2022) studied Atrial Fibrillation (n=5,862). Deep learning-based short single-lead ECG signal delineation model was evaluated on ECG waveform delineation and atrial fibrillation identification. A deep learning-based ECG signal delineation model accurately identified atrial fibrillation, achieving 98.91% sensitivity, 99.79% specificity, and 99.79% accuracy.
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