1. Deep learning for comprehensive ECG annotation.
- Author
-
Teplitzky BA, McRoberts M, and Ghanbari H
- Subjects
- Cardiac Complexes, Premature diagnosis, Humans, Predictive Value of Tests, Algorithms, Cardiac Complexes, Premature physiopathology, Deep Learning, Electrocardiography methods, Heart Rate physiology, Monitoring, Physiologic methods, Signal Processing, Computer-Assisted
- Abstract
Background: Increasing utilization of long-term outpatient ambulatory electrocardiographic (ECG) monitoring continues to drive the need for improved ECG interpretation algorithms., Objective: The purpose of this study was to describe the BeatLogic® platform for ECG interpretation and to validate the platform using electrophysiologist-adjudicated real-world data and publicly available validation data., Methods: Deep learning models were trained to perform beat and rhythm detection/classification using ECGs collected with the Preventice BodyGuardian® Heart monitor. Training annotations were created by certified ECG technicians, and validation annotations were adjudicated by a team of board-certified electrophysiologists. Deep learning model classification results were used to generate contiguous annotation results, and performance was assessed in accordance with the EC57 standard., Results: On the real-world validation dataset, BeatLogic beat detection sensitivity and positive predictive value were 99.84% and 99.78%, respectively. Ventricular ectopic beat classification sensitivity and positive predictive value were 89.4% and 97.8%, respectively. Episode and duration F
1 scores (range 0-100) exceeded 70 for all 14 rhythms (including noise) that were evaluated. F1 scores for 11 rhythms exceeded 80, 7 exceeded 90, and 5 including atrial fibrillation/flutter, ventricular tachycardia, ventricular bigeminy, ventricular trigeminy, and third-degree heart block exceeded 95., Conclusion: The BeatLogic platform represents the next stage of advancement for algorithmic ECG interpretation. This comprehensive platform performs beat detection, beat classification, and rhythm detection/classification with greatly improved performance over the current state of the art, with comparable or improved performance over previously published algorithms that can accomplish only 1 of these 3 tasks., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)- Published
- 2020
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