  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.
