Deep learning methods for screening patients' S-ICD implantation eligibility

TL;DR

This paper introduces a CNN-based method using phase space reconstruction to analyze ECG data over extended periods, improving the reliability of T:R ratio assessment for S-ICD eligibility screening.

Contribution

It presents a novel deep learning approach that predicts T:R ratios from ECG segments without wave detection, addressing temporal variability issues.

Findings

Enhanced screening reliability for S-ICD eligibility.

Ability to analyze T:R ratio dynamics over longer periods.

Potential reduction in TWOS risk through better screening.

Abstract

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.