摘要
心肌缺血早期检测是心血管疾病领域重要且困难的问题.本文采用心电动力学图(Cardiodynamicsgram,CDG)开展心电图正常及大致正常时的心肌缺血早期检测研究.1)在分析已有基于心电图的心肌缺血检测方法所取得的进展及不足基础上,构建一个既有心电图发生缺血性改变、又有心电图正常及大致正常、且包括经冠脉造影检验为冠脉阻塞性病变和非阻塞性病变的较大规模心肌缺血数据集.2)针对上述数据集中393例心电图正常及大致正常患者,利用确定学习生成每份心电图的心电动力学图,提取对心肌缺血和非缺血具有显著区分能力的心电动力学特征.并以冠脉狭窄50%为缺血标准,采用机器学习算法构建心肌缺血检测模型.3)针对上述试验中假阳性病例,利用由确定学习生成的具有明确物理意义的心电动力学图进行逐例分析,发现其中许多假阳性存在慢血流现象(即冠脉非阻塞性病变).对这些慢血流病例重新进行缺血标注,以改善心肌缺血数据集标注精度.通过上述三个步骤构建了更为准确的心肌缺血检测模型,其缺血检测结果:灵敏度90.1%、特异度85.2%、准确率89.0%和受试者工作特征曲线(Receiver operating characteristic curve,ROC)下面积(Area under curve,AUC)0.93.综上,本文所构建的较大规模心肌缺血数据集可为心肌缺血检测研究和临床研究提供重要的数据基础;且构建的心肌缺血检测模型对心电图正常及大致正常患者具有较强的缺血检测能力;特别是,由确定学习生成的心电动力学图具有较好的可解释性,有助于发现缺血数据标注的偏差和模型的错误,提高心肌缺血检测准确率.
Early detection of myocardial ischemia is a crucial and challenging problem in cardiovascular disease. In this paper, early detection of myocardial ischemia with normal or nearly normal electrocardiogram(ECG) is investigated by using cardiodynamicsgram(CDG). Firstly, by analyzing the advantages and disadvantages of existing ECG-based machine learning methods for myocardial ischemia detection, a relatively large-scale myocardial ischemia dataset is constructed, which contains ischemic, nearly normal and normal ECGs, as well as coronary stenosis and non-coronary stenosis detected by coronary angiography(CAG). Secondly, for 393 patients in the above dataset with normal or nearly normal ECGs, the deterministic learning algorithm is employed to generate CDGs,and the dynamical features underlying ECGs are extracted. The ischemia of patients is defined by coronary stenosis ≥ 50%, and the detection model of myocardial ischemia is established using machine learning algorithms. It is shown that ischemia detection model can distinguish myocardial ischemia from non-ischemia effectively. Thirdly, by analyzing the false-positive cases in the above trial, in which each CDG is generated with clear physical meanings,coronary slow flow phenomenons(i.e., non-obstructive coronary lesions) are found in many of the false-positive cases. These cases are re-labeled as ischemia, and a more accurate model for ischemia detection is constructed, with the sensitivity of 90.1%, specificity of 85.2%, accuracy of 89.0% and AUC(area under curve) of 0.93, respectively.As such, in this paper a relatively large-scale myocardial ischemia dataset is constructed which will provide an essential basis for future research on detection algorithms and clinical trials of myocardial ischemia. The established model has the ability to detect ischemia from patients with normal or nearly normal ECGs. Particularly, the CDGs generated by deterministic learning have favorable interpretability, which is helpful for finding the deviations of ischemic data labeling
作者
孙庆华
王磊
王聪
王乾
吴伟明
赵媛媛
王喜萍
董潇男
周彬
唐闽
SUN Qing-Hua;WANG Lei;WANG Cong;WANG Qian;WU Wei-Ming;ZHAO Yuan-Yuan;WANG Xi-Ping;DONG Xiao-Nan;ZHOU Bin;TANG Min(School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641;Department of Cardiology,Shihezi City Peoole's Hospital,Shihezi 832000;Scchool of Control Science and Engineering,Shandong University,Jinan 250061;Center for Intelligent Medical Engineering,Shandong University,Jinan 250061;Fuwai Hospital,Chinese Academy of Medical Sciences,Beijing 100037)
出处
《自动化学报》
EI
CSCD
北大核心
2020年第9期1908-1926,共19页
Acta Automatica Sinica
基金
国家重大科研仪器研制项目(61527811)
广州市科技计划项目(201704020078)
八师石河子市科技计划项目(2018TD03)资助。
关键词
心电动力学图
心肌缺血
确定学习
心电数据集
Cardiodynamicsgram(CDG)
myocardial ischemia
deterministic learning
electrocardiogram(ECG)dataset
