Early Detection of Myocardial Ischemia Based on Deterministic Learning and Cardiodynamicsgram
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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.Abstract: 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$ \ge$ 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 and model errors, and is capable of improving the accuracy of myocardial ischemia detection. -
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 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) 
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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 290 781 缺血病例 148 700 非缺血病例 52 81 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
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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±10 56±9 54±10 0.022* 收缩压 (mmHg) 129±10 131±18 127±14 0.563 舒张压 (mmHg) 77±10 85±14 77±10 0.109 心率 (beats/min) 72±10 65±6 71±10 0.012* 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.
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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 85.1 82.6 87.8 0.88 冠脉狭窄及慢血流 90.1 85.2 89.0 0.93
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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 72 KNN/SVM 96.00 93.00 99.00 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 22 SVM 92.69 80.96 80.96 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9 SVM 87.80 86.97 88.67 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10 KNN/SVM 81.71 79.01 79.26 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 95.22 95.49 94.19 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 softmax 95.49 94.85 97.37 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 2 SVM-Linear 97.00 98.65 92.31
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