Key result
EBTnet detects ST-segment and J-point deviations in noisy Holter ECGs with ~95% F1-score.
Why the study?
Few artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data, largely because interference signals from daily activities during Holter monitoring reduce detection performance.
A novel transformer-based deep neural network can accurately detect subtle ST-segment and J-point deviations in noisy Holter ECG signals, potentially aiding in screening for asymptomatic myocardial ischemia.
May support automated Holter analysis in noisy signals; leaves open prospective clinical validation before practice change.
Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSE de , SNR imp , and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system’s ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.
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Wu et al. (2022) studied Myocardial ischemia (n=431). ECG Bidirectional Transformer network (EBTnet) vs. Other deep learning models and cardiologist manual verification was evaluated on Segmentation model F1-score for QRS complex detection. The ECG Bidirectional Transformer network (EBTnet) accurately detected ST-segment and J-point deviations from noisy Holter ECG signals, achieving a segmentation F1-score of 94.51%.
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