Deep Learning-based Models For Complete Atrioventricular Block Heart Rhythm Analysis

Atrioventricular block (AVB), caused by impairment in the heart conduction system, presents extreme diversity and is associated with other complications. Only half of AVB patients require a permanent pacemaker, and the process determining the pacemaker implantation is associated with an increase in cost and patient morbidity and mortality. Thus, there is a need for models capable of accurately identifying transient or reversible causes for conduction disturbances and predicting the patient risks and the necessity of a pacemaker. Deep learning (DL) is brought to the forefront due to its prediction accuracy, and the DL-based electrocardiogram (ECG) analysis can be a breakthrough to analyze a massive amount of data. However, the current DL models are unsuitable for AVB-ECG, where the P waves are decoupled from the QRS/T waves, and a black-box nature of the DL-based model lowers the credibility of prediction models to physicians. Here, we present a real-time-capable DL-based algorithm that can identify AVB-ECG waves and automate AVB phenotyping for arrhythmogenic risk assessment. Our algorithm can analyze unformatted ECG records with abnormal patterns by integrating the two representative DL algorithms: convolutional neural networks (CNN) and recurrent neural networks (RNN). This hybrid CNN/RNN network can memorize local patterns, spatial hierarchies, and long-range temporal dependencies of ECG signals. Furthermore, by integrating parameters derived from dimension reduction analysis and heart rate variability into the hybrid layers, the algorithm can capture the P/QRS/T-specific morphological and temporal features in ECG waveforms. We evaluated the algorithm using the six AVB porcine models, where TBX18, a pacemaker transcription factor, was transduced into the ventricular myocardium to form a biological pacemaker, and an additional electronic pacemaker was transplanted as a backup pacemaker. We achieved high sensitivity (95% true positive rate) and quantified the potential risks of various pathological ECG patterns. This study may be a starting point in conducting both retrospective and prospective patient studies and will help physicians understand its decision-making workflow and find the incorrect recommendations for AVB patients.