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基于确定学习及心电动力学图的心肌缺血早期检测研究

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

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. 基于确定学习及心电动力学图的心肌缺血早期检测研究. 自动化学报, 2020, 46(9): 1908−1926 doi: 10.16383/j.aas.c190899
引用本文: 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. 基于确定学习及心电动力学图的心肌缺血早期检测研究. 自动化学报, 2020, 46(9): 1908−1926 doi: 10.16383/j.aas.c190899
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. Early detection of myocardial ischemia based on deterministic learning and cardiodynamicsgram. Acta Automatica Sinica, 2020, 46(9): 1908−1926 doi: 10.16383/j.aas.c190899
Citation: 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. Early detection of myocardial ischemia based on deterministic learning and cardiodynamicsgram. Acta Automatica Sinica, 2020, 46(9): 1908−1926 doi: 10.16383/j.aas.c190899

基于确定学习及心电动力学图的心肌缺血早期检测研究

doi: 10.16383/j.aas.c190899
基金项目: 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
详细信息
    作者简介:

    孙庆华:华南理工大学自动化科学与工程学院博士研究生. 主要研究方向为确定学习理论、动态模式识别及其在心肌缺血/心肌梗死/冠心病检测上的应用. E-mail: ausunqinghua@mail.scut.edu.cn

    王磊:石河子市人民医院(石河子大学医学院第三附属医院)心内科主治医师. 主要研究方向为冠心病. 共同第一作者. E-mail: wangleishitoukang@163.com

    王聪:山东大学控制科学与工程学院、山东大学智能医学工程研究中心教授. 主要研究方向为动态环境机器学习与模式识别, 确定学习理论, 基于模式的智能控制, 振动故障诊断及在医学领域的应用研究. 本文通信作者. E-mail: wangcong@sdu.edu.cn

    王乾:山东大学控制科学与工程学院博士后. 主要研究方向为确定学习, 故障诊断与健康预测. E-mail: auwangqian@sdu.edu.cn

    吴伟明:华南理工大学自动化科学与工程学院博士研究生. 主要研究方向为系统辨识, 确定学习, 动态模式识别. E-mail: auwuweiming@163.com

    赵媛媛:石河子市人民医院(石河子大学医学院第三附属医院)副主任护师. 主要研究方向为急性心肌梗死患者的护理.E-mail: zyy457027952@163.com

    王喜萍:石河子市人民医院(石河子大学医学院第三附属医院)心内科主任医师. 主要研究方向为冠心病.E-mail: wangxiping1567@163.com

    董潇男:中国医学科学院阜外医院医师. 主要研究方向为心律失常的诊断和介入治疗. E-mail: guitardxn@163.com

    周彬:中国医学科学院阜外医院博士研究生. 主要研究方向为心律失常. E-mail: zhoubinxhfw@163.com

    唐闽:中国医学科学院阜外医院主任医师. 主要研究方向为各种器质性心脏病、先天性心脏病和心功能不全合并心律失常的诊疗, 尤其是心房颤动、心房扑动、房性心动过速、室性早搏、室性心动过速、阵发性室上性心动过速等复杂心律失常的射频消融治疗和起搏器电极拔除治疗. 本文共同通信作者. E-mail: doctortangmin@hotmail.com

Early Detection of Myocardial Ischemia Based on Deterministic Learning and Cardiodynamicsgram

Funds: Supported by National Major Scientific Instruments Development Project (61527811), the Science and Technology Program of Guangzhou (201704020078), and the Science and Technology Program of Shihezi (2018TD03)
  • 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$ \ge$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.
  • 图  1  心肌缺血病因及临床类型

    Fig.  1  The causes and clinical presentation of myocardial ischemia

    图  2  心肌缺血诊断方法

    Fig.  2  Diagnostic methods of myocardial ischemia

    图  3  典型的心电图[34]

    Fig.  3  A standard electrocardiogram (ECG)[34]

    图  4  一例心肌缺血患者的心电动力学图及CDG值

    Fig.  4  The CDG and CDG value of a patient with myocardial ischemia

    图  5  冠脉狭窄与非狭窄组间的CDG值差异($ \lozenge $: $ p<0.01 $存在差异有高度统计显著性; $ \bigstar $: 超出边界的实例.)

    Fig.  5  Differences of CDG values between coronary stenosis and non-stenosis groups ($ \lozenge $: $ p<0.01 $ was considered as statistically significant. $ \bigstar $: subjects that were out of boundaries.)

    图  6  心电动力学图的心肌缺血检测结果

    Fig.  6  Results of myocardial ischemia detection via CDG

    图  7  一例冠脉单支病变男性患者, 55岁 ((a) 正常心电图; (b) 心电动力学图散乱;(c) 冠脉前降支存在80%狭窄;(d) CDG值阳性)

    Fig.  7  A case of ischemic male patient with single vessel disease, 55 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The left anterior descending branch of the coronary artery is with stenosis 80%; (d) The positive CDG value)

    图  8  一例冠脉双支病变男性患者, 35岁 ((a) 正常心电图; (b) 心电动力学图散乱; (c) 冠脉回旋支中段50%狭窄,右冠近段100%狭窄; (d) CDG值阳性)

    Fig.  8  A case of ischemic male patient with double-vessel disease, 35 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The middle segment of the left circumflex artery is with stenosis 50% narrow, and the proximal segment of the right coronary artery is occluded; (d) The positive CDG value)

    图  9  一例冠脉三支病变男性患者, 50岁 ((a) 正常心电图; (b) 心电动力学图散乱; (c) 中间支开口90%狭窄, 回旋支远段80%局限狭窄, 右冠远段90%局限狭窄; (d) CDG值阳性)

    Fig.  9  A case of ischemic male patient with triple-vessel disease, 50 years old ((a) Nondiagnostic ECG; (b) Irregular CDG; (c) The opening of the middle branch is 90% narrow, the distal segment of the left circumflex artery is with stenosis 80%, and the distal segment of the right coronary artery is with stenosis 90%; (d) The positive CDG value)

    图  10  一例非缺血女性患者, 47岁 ((a) 正常心电图; (b) 心电动力学图较为规整; (c) 正常冠脉; (d) CDG值阴性)

    Fig.  10  A case of nonischemic female patient, 47 years old ((a) Normal ECG; (b) Regular CDG; (c) Normal coronary angiography; (d) The negative CDG value)

    图  11  一例非缺血男性患者, 47岁 ((a) 正常心电图; (b) 心电动力学图规整; (c) 正常冠脉; (d) CDG值阴性)

    Fig.  11  A case of nonischemic male patient, 47 years old ((a) Normal ECG; (b) Regular CDG; (c) Normal coronary angiography; (d) The negative CDG value)

    图  12  不同缺血标注精度下分类模型的ROC曲线

    Fig.  12  ROC curves of classification models at different accuracy of ischemic labeling

    图  13  一例慢血流男性患者, 48岁 ((a) 正常心电图; (b) 心电动力学图散乱; (c) 冠脉无狭窄前降支慢血流; (d) CDG值阳性)

    Fig.  13  A case of ischemic male patient with slow coronary flow, 48 years old ((a) Normal ECG; (b) Irregular CDG; (c) The left anterior descending branch of the coronary artery is with coronary slow flow; (d) The positive CDG value)

    图  14  一例慢血流女性患者, 50岁 ((a) 正常心电图; (b) 心电动力学图散乱; (c) 冠脉无狭窄但前降支中段第一对角支慢血流; (d) CDG值阳性)

    Fig.  14  A case of ischemic female patient with slow coronary flow, 50 years old ((a) Normal ECG; (b) Irregular CDG; (c) Coronary slow flow in the first diagonal branch of the coronary artery; (d) The positive CDG value)

    表  1  疑似心肌缺血患者病例信息记录

    Table  1  A case of suspected myocardial ischemic patient

    项目信息记录
    编号/来源SHZ2944/石河子市人民医院
    年龄/性别59/男
    心率/血压68 (次/分)/200 (高), 100 (低) (mmHg)
    主诉半月前无诱因再次出现胸骨中下段拳头大小范围压迫样疼痛, 伴胸闷、心慌、出汗, 症状持续数分钟休息后缓解, 症状频繁发作, 偶有静息下发作
    既往史平素健康状况一般, 高血压 30 年, 最高达 200/100 mmHg, 无其他病史
    心电图窦性心律, 偶发室早, T波改变
    冠脉造影前降支近段斑块; 回旋支近段斑块、远段 100% 闭塞, 可见前降支到回旋支侧枝形成; 右冠中段 80% 病变, 远段 90% 弥漫性病变
    临床诊断1) 冠心病, 不稳定性心绞痛; 2) 高血压 3 级 (很高危)
    下载: 导出CSV

    表  2  自建数据集与PTB数据集对比

    Table  2  Comparison between PTB diagnostic dataset and the proposed dataset

    来源PTB自建
    总病例数290781
    缺血病例148700
    非缺血病例5281
    心电图基本发生缺血性改变393 例正常或非特异性改变
    缺血病因冠脉狭窄冠脉狭窄、慢血流
    下载: 导出CSV

    表  3  心电图正常或大致正常患者的人口基线特征

    Table  3  Baseline characteristics of patients with normal or nearly normal ECG

    类型冠脉狭窄 (299)非冠脉狭窄 (n = 94)p 值
    冠脉慢血流 (13)非冠脉病变 (81)
    性别 (男性)216/299 (72.2%)9/13 (69.2%)43/81 (53.7%)0.005**
    年龄58±1056±954±100.022*
    收缩压 (mmHg)129±10131±18127±140.563
    舒张压 (mmHg)77±1085±1477±100.109
    心率 (beats/min)72±1065±671±100.012*
    高血压171/299 (57.2%)10/13 (76.9%)42/81 (51.9%)0.226
    糖尿病88/299 (29.4%)6/13 (46.2%)18/81 (22.2%)0.159
    血脂异常190/299 (63.5%)8/13 (61.5%)53/81 (65.4%)0.937
    注: 所有数据采用软件 SPSS 21.0 进行统计分析; 计量资料采用 Mann-Whitney 秩和检验, 表示为 (均值±标准差); 计数资料采用卡方检验, 用%表示; *: p < 0.05为差异有统计显著性; **: p < 0.01为差异有高度统计显著性.
    下载: 导出CSV

    表  4  不同缺血标注精度下, 心电动力学图的缺血检测结果

    Table  4  The results of CDG in the detection of ischemia at different precision of ischemia labeling

    缺血标准灵敏度 (%)特异度 (%)准确率 (%)AUC
    冠脉狭窄85.182.687.80.88
    冠脉狭窄及慢血流90.185.289.00.93
    下载: 导出CSV

    表  5  本文方法与文献中的方法在PTB数据集上的心肌缺血检测结果对比

    Table  5  Comparison of the CDG against the related literatures about myocardial ischemia detection

    方法数据方法特点特征数分类器性能 (%)
    准确率敏感度特异度
    Sharma等 (2015)[20]导联: 12 导联心电记录: 148 MI, 52 HC多尺度小波能量特征72KNN/SVM96.0093.0099.00
    Han等 (2019)[15]导联: 12 导联心电记录: 28 213 MI,
    5 373 HC
    能量熵; 形态学特征22SVM92.6980.9680.96
    Diker等 (2018[17]导联: 不可知心电信号: 148 MI, 52 HC形态学特征; 时域特征;
    离散小波变换特征
    9SVM87.8086.9788.67
    Sharma等 (2018)[18]导联: II、III、aVF 导联心电信号: 3 240
    下壁 MI, 3 037 HC
    样本熵; 归一化子带能量;
    对数能量熵; 中值斜率
    10KNN/SVM81.7179.0179.26
    Acharya等 (2017)[27]导联: II 导联心拍: 40 182 MI, 10 546 HC卷积神经网络全连接网络95.2295.4994.19
    Han等 (2020)[29]导联: 12 导联心电记录: 17 212 MI,
    6 945 HC
    多导联残差网络全连接 softmax95.4994.8597.37
    本文方法导联: 12 导联心电记录: 148 MI, 52 HC心电动力学图特征2SVM-Linear97.0098.6592.31
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-31
  • 录用日期:  2020-06-11
  • 网络出版日期:  2020-09-28
  • 刊出日期:  2020-09-28

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