Liu Liong-Rung, Huang Ming-Yuan, Huang Shu-Tien, Kung Lu-Chih, Lee Chao-Hsiung, Yao Wen-Teng, Tsai Ming-Feng, Hsu Cheng-Hung, Chu Yu-Chang, Hung Fei-Hung, Chiu Hung-Wen
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan.
Heliyon. 2024 Feb 29;10(5):e27200. doi: 10.1016/j.heliyon.2024.e27200. eCollection 2024 Mar 15.
Arrhythmia, a frequently encountered and life-threatening cardiac disorder, can manifest as a transient or isolated event. Traditional automatic arrhythmia detection methods have predominantly relied on QRS-wave signal detection. Contemporary research has focused on the utilization of wearable devices for continuous monitoring of heart rates and rhythms through single-lead electrocardiogram (ECG), which holds the potential to promptly detect arrhythmias. However, in this study, we employed a convolutional neural network (CNN) to classify distinct arrhythmias without QRS wave detection step. The ECG data utilized in this study were sourced from the publicly accessible PhysioNet databases. Taking into account the impact of the duration of ECG signal on accuracy, this study trained one-dimensional CNN models with 5-s and 10-s segments, respectively, and compared their results. In the results, the CNN model exhibited the capability to differentiate between Normal Sinus Rhythm (NSR) and various arrhythmias, including Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Wolff-Parkinson-White syndrome (WPW), Ventricular Fibrillation (VF), Ventricular Tachycardia (VT), Ventricular Flutter (VFL), Mobitz II AV Block (MII), and Sinus Bradycardia (SB). Both 10-s and 5-s ECG segments exhibited comparable results, with an average classification accuracy of 97.31%. It reveals the feasibility of utilizing even shorter 5-s recordings for detecting arrhythmias in everyday scenarios. Detecting arrhythmias with a single lead aligns well with the practicality of wearable devices for daily use, and shorter detection times also align with their clinical utility in emergency situations.
心律失常是一种常见且危及生命的心脏疾病，可表现为短暂或孤立事件。传统的自动心律失常检测方法主要依赖于QRS波信号检测。当代研究聚焦于利用可穿戴设备通过单导联心电图（ECG）对心率和心律进行连续监测，这有可能及时检测到心律失常。然而，在本研究中，我们采用卷积神经网络（CNN）在不进行QRS波检测步骤的情况下对不同的心律失常进行分类。本研究中使用的ECG数据来自可公开获取的PhysioNet数据库。考虑到ECG信号时长对准确性的影响，本研究分别用5秒和10秒的片段训练一维CNN模型，并比较其结果。结果显示，CNN模型能够区分正常窦性心律（NSR）和各种心律失常，包括心房颤动（AFIB）、心房扑动（AFL）、预激综合征（WPW）、心室颤动（VF）、室性心动过速（VT）、心室扑动（VFL）、莫氏Ⅱ型房室传导阻滞（MII）和窦性心动过缓（SB）。10秒和5秒的ECG片段结果相当，平均分类准确率为97.31%。这揭示了在日常场景中利用甚至更短的5秒记录来检测心律失常的可行性。用单导联检测心律失常与日常使用的可穿戴设备的实用性相契合，更短的检测时间也与它们在紧急情况下的临床效用相契合。
