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Explainable localization of premature ventricular contraction using deep learning‐based semantic segmentation of 12‐lead electrocardiogram

Authors :
Kota Kujime
Hiroshi Seno
Kenzaburo Nakajima
Masatoshi Yamazaki
Ichiro Sakuma
Kenichiro Yamagata
Kengo Kusano
Naoki Tomii
Source :
Journal of Arrhythmia, Vol 40, Iss 4, Pp 948-957 (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

Abstract Background Predicting the origin of premature ventricular contraction (PVC) from the preoperative electrocardiogram (ECG) is important for catheter ablation therapies. We propose an explainable method that localizes PVC origin based on the semantic segmentation result of a 12‐lead ECG using a deep neural network, considering suitable diagnosis support for clinical application. Methods The deep learning‐based semantic segmentation model was trained using 265 12‐lead ECG recordings from 84 patients with frequent PVCs. The model classified each ECG sampling time into four categories: background (BG), sinus rhythm (SR), PVC originating from the left ventricular outflow tract (PVC‐L), and PVC originating from the right ventricular outflow tract (PVC‐R). Based on the ECG segmentation results, a rule‐based algorithm classified ECG recordings into three categories: PVC‐L, PVC‐R, as well as Neutral, which is a group for the recordings requiring the physician's careful assessment before separating them into PVC‐L and PVC‐R. The proposed method was evaluated with a public dataset which was used in previous research. Results The evaluation of the proposed method achieved neutral rate, accuracy, sensitivity, specificity, F1‐score, and area under the curve of 0.098, 0.932, 0.963, 0.882, 0.945, and 0.852 on a private dataset, and 0.284, 0.916, 0.912, 0.930, 0.943, and 0.848 on a public dataset, respectively. These quantitative results indicated that the proposed method outperformed almost all previous studies, although a significant number of recordings resulted in requiring the physician's assessment. Conclusions The feasibility of explainable localization of premature ventricular contraction was demonstrated using deep learning‐based semantic segmentation of 12‐lead ECG. Clinical trial registration: M26‐148‐8.

Details

Language :
English
ISSN :
18832148 and 18804276
Volume :
40
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Arrhythmia
Publication Type :
Academic Journal
Accession number :
edsdoj.3a9e983ac40e47d5acda9ea591600b26
Document Type :
article
Full Text :
https://doi.org/10.1002/joa3.13096