School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China.
School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China.
Math Biosci Eng. 2023 Jun 5;20(7):13061-13085. doi: 10.3934/mbe.2023582.
Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD.
To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD.
Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme.
ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8.
Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.
冠状动脉微血管功能障碍（CMD）正逐渐成为心肌缺血的一个重要原因，但目前缺乏一种可靠的非侵入性方法来早期检测 CMD。
开发一种基于心电图（ECG）的机器学习算法，用于 CMD 检测，为患者特异性 CMD 早期非侵入性检测奠定基础。
从 CMD 患者和健康对照者的每 10 秒 ECG 中计算向量心电图（VCG）。从 ECG 和 VCG 的每个导联的 ST-T 段中提取来自多尺度熵的样本熵（SampEn）、近似熵（ApEn）和复杂度指数（CI）。使用序贯后向选择算法，分别在患者内和患者间方案下确定最有效的熵子集。然后，根据五重交叉验证，从 8 种机器学习模型中为每个熵特征选择相应的最佳模型。最后，在测试数据集上综合评估和测试基于 SampEn、ApEn 和 CI 的模型的分类性能，以研究每种方案下的最佳模型。
ApEn 基于 SVM 模型在患者内方案下被验证为最佳模型，所有测试评估指标均超过 0.8。同样，ApEn 基于 SVM 模型在患者内方案下被选为最佳模型，主要评估指标均超过 0.8。
从 ECG 和 VCG 中提取的熵可以有效地在患者内和患者间方案下检测 CMD。我们提出的模型可能为基于 ECG 的 CMD 非侵入性检测提供一种工具的可能性。
