Department of Information Technology Engineering, Industrial and Systems Engineering Faculty, Tarbiat Modares University, Tehran, Iran.
J Healthc Eng. 2021 Apr 22;2021:6624829. doi: 10.1155/2021/6624829. eCollection 2021.
One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient's electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient's heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient's life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.
世界上主要的死亡原因之一是心律失常。在医疗保健领域，医生使用患者的心电图（ECG）记录来检测心律失常，这表明患者心脏的电活动。问题是症状并不总是出现，医生可能会在诊断中出错。因此，患者需要通过实时 ECG 分析进行持续监测，以便及时检测心律失常，防止最终危及患者生命的事件发生。在这项研究中，我们首次使用构建在开源 Apache Spark 平台之上的 Structured Streaming 模块来实现实时心脏心律失常检测的机器学习管道，并评估使用这个新模块对分类性能指标和心律失常检测延迟率的影响。我们从 MIT/BIH 数据库中收集 ECG 数据，用于检测三个类别标签：正常心跳、RBBB 和心房颤动心律失常。我们还为数据分类开发了三个决策树、随机森林和逻辑回归多类分类器，其中随机森林分类器在分类性能方面优于其他两个分类器。结果表明，与之前的研究相比，该分类模型的性能指标和管道运行时间都有了显著的提高，并且使用了更多的类别标签。
