Zhao Xiaoye, Zhang Jucheng, Gong Yinglan, Xu Lihua, Liu Haipeng, Wei Shujun, Wu Yuan, Cha Ganhua, Wei Haicheng, Mao Jiandong, Xia Ling
School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.
School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.
Front Physiol. 2022 May 30;13:854191. doi: 10.3389/fphys.2022.854191. eCollection 2022.
Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (, of VCG) and temporal heterogeneity index (, of VCG) are calculated. Using a grid search, four and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features ( , , and , where is the of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. The ECG + VCG model with three features ( ,, and ) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.
心肌缺血是心血管疾病（CVD）常见的早期症状。利用心电图（ECG）的计算机辅助分析可靠地检测心肌缺血可为CVD的早期诊断提供重要参考。向量心电图（VCG）通过提供与心肌缺血相关的时空特征并捕捉连续心动周期中ST-T段的细微变化，可提高基于ECG的心肌缺血检测性能。我们旨在研究ECG和VCG的组合是否能提高机器学习算法在自动心肌缺血检测中的性能。从377例心肌缺血患者和52例健康对照中提取20秒12导联ECG和VCG的ST-T段。然后，计算12个ECG导联和3个VCG导联的样本熵（ ）、VCG的空间异质性指数（ ）和时间异质性指数（ ）。使用网格搜索，分别为基于支持向量机（SVM）的仅ECG模型和仅VCG模型选择4个和2个特征作为输入信号特征。同样，为ECG + VCG模型进一步选择3个特征（ 、 和 ，其中 是I导联的 ）。采用5折交叉验证评估仅ECG、仅VCG和ECG + VCG模型的性能。为全面评估算法的泛化能力，选择性能最佳的模型并在由148例心肌缺血患者和52例健康对照组成的第三个独立数据集上进行测试。具有3个特征（ 、 和 ）的ECG + VCG模型比仅ECG和仅VCG模型产生更好的分类结果，平均准确率为0.903，灵敏度为0.903，特异性为0.905，F1分数为0.942，AUC为0.904，与现有研究相比，在特征较少的情况下表现更好。在第三个独立数据集上，测试显示AUC为0.814。基于ECG + VCG模型的SVM算法能够可靠地检测心肌缺血，为基层医疗服务常规筛查中协助心脏病专家进行CVD的早期诊断提供了一种潜在工具。
