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.人工智能在临床心电图（electrocardiogram, ECG）工作流程中的应用愈发广泛。当前基于人工智能算法的心电图研究中，鲜有聚焦于利用长时程心电图数据检测心肌缺血（myocardial ischemia）的工作。究其核心原因，受试者佩戴动态心电图记录仪（Holter monitor）时日常活动产生的干扰信号，会削弱人工智能检测心肌缺血的效能。本研究开发了一套集成去噪与分割模块的自动系统，用于检测ST段（ST-segment）与J点（J point）的偏移情况。我们提出了一种可同时适配去噪与分割任务的心电图双向Transformer网络。该去噪模型的均方根差（RMSEde）、信噪比改善量（SNRimp）与百分比均方根差（PRD）分别为0.074、10.006与16.327。分割模型的精确率、灵敏度（召回率）与F1分数分别达到96.00、93.06与94.51%。此外，该系统区分ST段与J点压低、抬高的能力，亦得到了心脏病学家的验证。在本研究的心电图数据集中，共检出103例ST段压低患者与10例ST段抬高患者，二者的阳性预测值分别为80.6%与60%。通过结合动态心电图数据与基于Transformer的深度神经网络，我们能够在含噪心电图信号中识别出细微的ST段变化。本系统有望提升日常临床诊疗效率，并可为无症状心肌缺血患者提供更具普适性的人群级筛查。