Table1_Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.DOCX

Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSEde, SNRimp, and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system’s ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.

人工智能技术在临床心电图流程中的应用日益广泛。基于人工智能算法的心电图数据中，少部分研究聚焦于利用长期心电图数据检测心肌缺血。其主因在于，佩戴Holter监测器时由日常活动产生的干扰信号削弱了人工智能检测心肌缺血的能力。在本研究中，我们开发了一个结合降噪和分割模块的自动系统，以检测ST段和J点的偏移。我们提出了一种应用于降噪和分割任务的ECG双向Transformer网络。降噪模型分别实现了RMSEde、SNRimp和PRD值为0.074、10.006和16.327。分割模型分别达到了96.00%、93.06%和94.51%的精确度、敏感度（召回率）和F1分数。该系统区分ST段和J点的下陷与上升的能力也得到了心脏病学家的验证。从我们的心电图数据集中，检测到103例ST段下陷患者和10例ST段上升患者，其阳性预测值分别为80.6%和60%。利用Holter心电图和基于Transformer的深度神经网络，我们能够在嘈杂的心电图信号中检测到细微的ST段变化。该系统具有提高日常药物疗效的潜力，并为无症状心肌缺血提供更广泛的群体水平筛查。