Early Detection of Coronary Microvascular Dysfunction Using Machine Learning Algorithm Based on Vectorcardiography and Cardiodynamicsgram Features

• Published in: Ingénierie et recherche biomédicale 2023-12, Vol.44 (6), p.100805, Article 100805
• Main Authors: Zhao, Xiaoye, Gong, Yinglan, Zhang, Jucheng, Liu, Haipeng, Huang, Tianhai, Jiang, Jun, Niu, Yanli, Xia, Ling, Mao, Jiandong
• Format: Article
• Language: English
• Subjects: Cardiodynamicsgram (CDG); Coronary microvascular dysfunction (CMD); Entropy; Multilayer perceptron (MLP); Sparrow search algorithm (SSA); Vectorcardiogram (VCG)
• ISSN: 1959-0318
• DOI: 10.1016/j.irbm.2023.100805

As a main etiology of myocardial ischemia, coronary microvascular dysfunction (CMD) can occur in patients with or without obstructive coronary artery disease. Currently, there is a lack of a non-invasive approach for early detection of CMD. We aim to develop a multilayer perceptron (MLP) algorithm to achieve non-invasive early detection of CMD based on vectorcardiography (VCG) and cardiodynamicsgram (CDG) features. Electrocardiograms of 82 CMD patients and 107 healthy controls were collected and synthesized into VCGs. The VCGs' ST-T segments were extracted and fed into a deterministic learning algorithm to develop CDGs. Temporal heterogeneity index, spatial heterogeneity index, sample entropy, approximate entropy, and complexity index were extracted from VCGs' ST-T segments and CDGs, entitled as STT- and CDG-based features, respectively. The most effective feature subsets were determined from CDG-based, STT-based, and the combined features (i.e., all features) via the sequential backward selection algorithm as inputs for CDG-, STT-, and CDG-STT-based MLP models optimized with an improved sparrow search algorithm, respectively. Finally, the classification capacity of the corresponding models was evaluated via five-fold cross-validations and tested on a testing dataset to verify the optimal one. The CDG-STT-based MLP model had significantly higher evaluated metrics than CDG- and STT-based ones on the validation dataset, with the accuracy, sensitivity, specificity, F1 score, and AUC of 0.904, 0.925, 0.870, 0.870, and 0.897 on the testing dataset respectively. The MLP model based on VCG and CDG features showed high efficiency in identifying CMD. The CDG-STT-based MLP model may afford a potential computer-aided tool for non-invasive detection of CMD. •Non-invasive early detection of coronary microvascular dysfunction (CMD).•Vectorcardiogram (VCG) and cardiodynamicsgram (CDG) features for CMD detection.•Our proposed multilayer perceptron model: accuracy of 0.904 and sensitivity of 0.925.•Temporal and spatial features of VCG and CDG are effective indicators of a CMD.