Robust PVC Identification by Fusing Expert System and Deep Learning

Abstract

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

“…Continuously monitoring and recognition for life threating arrhythmia based on wearable and smartphone devices using deep-learning method become a hot topic in recent years [21][22][23]. As PVC often exhibits no obvious clinical symptoms during the attack and is a primary contributor to sudden cardiac death, there have been many research results on the real-time recognition of PVCs [24][25][26][27]. Brito et al [28] proposed a deep learning model based on ResNet architecture in 2019, achieving an accuracy of over 90% in experiments using the MIT-BIH arrhythmia database.…”