Key result
MLP5 deep learning outperforms CNN5 in predicting ECG T:R ratios with an RMSE of 0.105.
Why the study?
Manual template-based screening of 10-second ECG segments to assess T:R ratios for S-ICD eligibility can be unreliable due to temporal variability, prompting the need for automated screening over longer windows.
Does a deep learning-based tool improve the accuracy of T:R ratio prediction for S-ICD eligibility compared to manual 10-second screening?
Does a deep learning-based tool improve the accuracy of T:R ratio prediction for S-ICD eligibility compared to manual 10-second screening?
A novel deep learning tool can automate and accurately predict T:R ratios from 24-hour ECG recordings, potentially improving the reliability of S-ICD eligibility screening compared to standard manual 10-second screening.
Answers are grounded in this paper and cite their sources.
May support automated T:R prediction for S-ICD eligibility; hypothesis-generating and requires prospective validation.
It is standard cardiology practice for patients suffering from ventricular arrhythmias (the main cause of sudden cardiac death) belonging to high risk populations to be treated via the implantation of Subcutaneous Implantable cardioverter-defibrillators (S-ICDs). S-ICDs carry a risk of so-called T wave over sensing (TWOS), which can lead to inappropriate shocks that carry an inherent health risk. For this reason, according to current practice patients’ Electrocardiograms (ECGs) are manually screened by a cardiologist over 10 s to assess the T:R ratio—the ratio between the amplitudes of the T and R waves which is used as a marker for the likelihood of TWOS—with a plastic template. Unfortunately, the temporal variability of a patient’ T:R ratio can render such a screening procedure, which relies on an inevitably short ECG segment due to its manual nature, unreliable. In this paper, we propose and investigate a tool based on deep learning for the automatic prediction of the T:R ratios from multiple 10-second segments of ECG recordings capable of carrying out a 24-hour automated screening. Thanks to the significantly increased screening window, such a screening would provide far more reliable T:R ratio predictions than the currently utilized 10-second, template-based, manual screening is capable of. Our tool is the first, to the best of our knowledge, to fully automate such an otherwise manual and potentially inaccurate procedure. From a methodological perspective, we evaluate different deep learning model architectures for our tool, assess a range of stochastic-gradient-descent-based optimization methods for training their underlying deep-learning model, perform hyperparameter tuning, and create ensembles of the best performing models in order to identify which combination leads to the best performance. We find that the resulting model, which has been integrated into a prototypical tool for use by clinicians, is able to predict T:R ratios with very high accuracy. Thanks to this, our automated T:R ratio detection tool will enable clinicians to provide a completely automated assessment of whether a patient is eligible for S-ICD implantation which is more reliable than current practice thanks to adopting a significantly longer ECG screening window which better and more accurately captures the behavior of the patient’s T:R ratio than the current manual practice.
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Dunn et al. (2023) studied Ventricular arrhythmias (S-ICD eligibility) (n=108). MLP5 deep learning model vs. Complex CNN5 model was evaluated on T:R ratio prediction accuracy (RMSE).
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