An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus rhythm

Abstract

Background Ventricular premature complex (VPC) is a common arrhythmia in clinical practice. VPC could trigger ventricular tachycardia/fibrillation or VPC-induced cardiomyopathy in susceptible patients. Existing screening methods require prolonged monitoring and are limited by cost and low yield when the frequency of VPC is low. Twelve-lead electrocardiogram (ECG) is low cost and widely used. We aimed to identify patients with VPC during normal sinus rhythm (NSR) using artificial intelligence (AI) and machine learning-based ECG reading. Methods We developed AI-enabled ECG algorithm using a convolutional neural network (CNN) to detect the ECG signature of VPC presented during NSR using standard 12-lead ECGs. A total of 2515 ECG records from 398 patients with VPC were collected. Among them, only ECG records of NSR without VPC (1617 ECG records) were parsed. Results A total of 753 normal ECG records from 387 patients under NSR were used for comparison. Both image and time-series datasets were parsed for the training process by the CNN models. The computer architectures were optimized to select the best model for the training process. Both the single-input image model (InceptionV3, accuracy: 0.895, 95% confidence interval [CI] 0.683–0.937) and multi-input time-series model (ResNet50V2, accuracy: 0.880, 95% CI 0.646–0.943) yielded satisfactory results for VPC prediction, both of which were better than the single-input time-series model (ResNet50V2, accuracy: 0.840, 95% CI 0.629–0.952). Conclusions AI-enabled ECG acquired during NSR permits rapid identification at point of care of individuals with VPC and has the potential to predict VPC episodes automatically rather than traditional long-time monitoring.

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“…In a similar example, ML has superior data discrimination compared to humans. For example, ML models have been able to detect ventricular premature complexes during sinus rhythm, not during arrhythmia, and it is difficult for even highly trained medical doctors to predict ventricular premature complexes during normal rhythms 32 . The authors discussed a possible mechanism by which MLM can detect subtle ECG structural changes that underline ventricular premature complexes.…”

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“…Chang et al. developed a CNN model to detect VPC during normal sinus rhythm ECG, which yields satisfactory results (accuracy:89.5%) for VPC prediction 63 . Accurate rhythm diagnosis is critical in patients presenting with wide QRS complex tachycardia (WCTs).…”