Deep learning methods for screening patients' S-ICD implantation eligibility
This addresses the need for more reliable patient screening to prevent inappropriate shocks in S-ICD implantation, though it is incremental as it applies existing deep learning methods to a specific medical domain.
The paper tackled the problem of unreliable screening for Subcutaneous Implantable Cardioverter-Defibrillator (S-ICD) eligibility due to temporal variations in T:R ratios from short ECG segments, by developing a CNN-based model that predicts T:R ratios from 10-second ECG segments without explicit wave localization, enabling automated screening over longer periods.
Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently patients' Electrocardiograms (ECGs) are screened over 10 seconds to measure the T:R ratio, determining the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 seconds is not long enough to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behaviour of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.