Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Comput Biol Med. 2022 Jul;146:105584. doi: 10.1016/j.compbiomed.2022.105584. Epub 2022 May 5.
Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the atria, and may cause heart thrombosis, stroke, arterial disease, thromboembolism, and heart failure. Prior to the onset of atrial fibrillation, most people experience atrial cardiomyopathy which, if effectively managed, can be prevented from progressing to atrial fibrillation. Electrocardiogram (ECG) can show changes in the heartbeats, and is a common and painless tool to detect heart problems. P-waves in exercise ECGs change more drastically than those in regular ECG, and are more effective in the detection of atrial myocardial diseases. In this paper, we propose a deep learning system to help clinicians to early detect if a patient has atrial enlargement or fibrillation. Firstly, a Convolutional Recurrent Neural Network is employed to locate the P-waves in the patient's exercise ECGs taken in the exercise ECG test process. Relevant parameters are then calculated from the located P-waves. Then a Parallel Bi-directional Long Short-Term Memory Network is applied to analyze the obtained parameters and make a diagnosis for the patient. With our proposed deep learning system, the changes of P-waves collected in different phases in the exercise ECG test can be analyzed simultaneously to get more stable and accurate results. The system can take data of different length as input, and is also applicable to any number of ECG collections. We conduct various experiments to show the effectiveness of our proposed system. We also show that the more ECG data collected in the exercise phase are involved, the more effective our system is in diagnosis of the diseases.
心房颤动（AF）是最常见的持续性心律失常类型。它是由于心房电活动异常不规则引起的，可能导致心脏血栓形成、中风、动脉疾病、血栓栓塞和心力衰竭。在心房颤动发作之前，大多数人经历心房心肌病，如果得到有效治疗，可以防止其进展为心房颤动。心电图（ECG）可以显示心跳变化，是一种常见且无痛的检测心脏问题的工具。运动心电图中的 P 波变化比常规心电图中的 P 波变化更为剧烈，对于检测心房心肌疾病更为有效。在本文中，我们提出了一种深度学习系统，以帮助临床医生早期检测患者是否存在心房扩大或颤动。首先，使用卷积递归神经网络（Convolutional Recurrent Neural Network）定位患者在运动心电图测试过程中采集的运动心电图中的 P 波。然后从定位的 P 波中计算相关参数。然后应用并行双向长短期记忆网络（Parallel Bi-directional Long Short-Term Memory Network）分析获得的参数并为患者做出诊断。通过我们提出的深度学习系统，可以同时分析运动心电图测试中不同阶段采集的 P 波变化，以获得更稳定和准确的结果。该系统可以接受不同长度的数据作为输入，也适用于任意数量的心电图采集。我们进行了各种实验以证明我们提出的系统的有效性。我们还表明，在运动阶段采集的心电图数据越多，我们的系统在诊断疾病方面就越有效。
