School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 714009, China.
Biosensors (Basel). 2022 Mar 22;12(4):185. doi: 10.3390/bios12040185.
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.
室性期前收缩（PVC）是常见的室性心律失常之一，可导致中风或心源性猝死。自动长期心电图（ECG）分析算法可为医生提供诊断建议甚至预警。然而，它们在稳健性、泛化能力和低复杂性方面相互排斥。在这项研究中，提出了一种将基于深度学习的心搏模板聚类器与基于专家系统的心搏分类器相结合的新型 PVC 识别算法。基于长短期记忆的自动编码器（LSTM-AE）网络用于从 ECG 心搏中提取特征进行 K-均值聚类。因此，基于聚类结果构建和确定模板。最后，基于多个规则（包括模板匹配和节律特征）来识别 PVC 心搏。灵敏度（Se）、阳性预测值（P+）和准确性（ACC）三个定量参数用于评估该方法在 MIT-BIH 心律失常数据库和圣彼得堡心血管技术研究所数据库上的性能。在两个测试数据库上的 Se 分别为 87.51%和 87.92%；P+分别为 92.47%和 93.18%；ACC 分别为 98.63%和 97.89%。在第三届中国生理信号挑战赛 2020 训练集和隐藏测试集上的 PVC 得分为 36256 和 46706，在开源代码中排名第一。结果表明，专家系统与深度学习相结合的策略可为长期单导联 ECG 记录中稳健且通用的 PVC 识别提供新的思路。
