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Deep learning methods for screening patients' S-ICD implantation eligibility.
- Source :
-
Artificial Intelligence in Medicine . Sep2021, Vol. 119, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
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 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window 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 behavior 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMPLANTABLE cardioverter-defibrillators
*SUDDEN death
*DEEP learning
*RAYLEIGH waves
*CONVOLUTIONAL neural networks
*SUDDEN death prevention
*CARDIAC arrest
*ARRHYTHMIA treatment
*ARRHYTHMIA diagnosis
*RESEARCH
*RESEARCH methodology
*MEDICAL screening
*EVALUATION research
*COMPARATIVE studies
*ELECTROCARDIOGRAPHY
Subjects
Details
- Language :
- English
- ISSN :
- 09333657
- Volume :
- 119
- Database :
- Academic Search Index
- Journal :
- Artificial Intelligence in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 152426613
- Full Text :
- https://doi.org/10.1016/j.artmed.2021.102139