Wu Shuang, Cao Qing, Chen Qiaoran, Jin Qi, Liu Zizhu, Zhuang Lingfang, Lin Jingsheng, Lv Gang, Zhang Ruiyan, Chen Kang
Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Physiol. 2022 Jun 29;13:912739. doi: 10.3389/fphys.2022.912739. eCollection 2022.
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 RMSE, SNR, 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工作流程中的应用越来越广泛。基于人工智能算法的心电图很少专注于利用长期心电图数据检测心肌缺血。造成这种情况的一个主要原因是，佩戴动态心电图监测仪时日常活动产生的干扰信号降低了人工智能检测心肌缺血的能力。在本研究中，开发了一个结合去噪和分割模块的自动系统来检测ST段和J点的偏移。我们提出了一种应用于去噪和分割任务的心电图双向变压器网络。去噪模型的均方根误差（RMSE）、信噪比（SNR）和百分比残留差异（PRD）值分别为0.074、10.006和16.327。分割模型的精确率、灵敏度（召回率）和F1分数分别为96.00%、93.06%和94.51%。心脏病专家也验证了该系统区分ST段和J点压低与抬高的能力。从我们的心电图数据集中，检测出103例ST段压低患者和10例ST段抬高患者，阳性预测值分别为80.6%和60%。利用动态心电图和基于变压器的深度神经网络，我们可以检测嘈杂心电图信号中细微的ST段变化。该系统有可能提高日常医疗的疗效，并为无症状心肌缺血提供更广泛的人群水平筛查。
