Bibliographic Details
| Title: |
Deep learning methods for screening patients' S-ICD implantation eligibility |
| Authors: |
Dunn, Anthony J., ElRefai, Mohamed H., Roberts, Paul R., Coniglio, Stefano, Wiles, Benedict M., Zemkoho, Alain B. |
| Publication Year: |
2021 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2103.06021 |
| Accession Number: |
edsarx.2103.06021 |
| Database: |
arXiv |
