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

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-09-28 DOI:10.1016/j.irbm.2023.100805
Xiaoye Zhao , Yinglan Gong , Jucheng Zhang , Haipeng Liu , Tianhai Huang , Jun Jiang , Yanli Niu , Ling Xia , Jiandong Mao
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引用次数: 0

Abstract

Purpose

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.

Aim

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.

Methods

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.

Results

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.

Conclusions

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.

Abstract Image

基于矢量心动图和心动力图特征的机器学习算法早期检测冠状动脉微血管功能障碍
目的冠状动脉微血管功能障碍(CMD)作为心肌缺血的主要病因,可发生在患有或不患有阻塞性冠状动脉疾病的患者中。目前,缺乏一种用于CMD早期检测的非侵入性方法。目的基于心电向量图(VCG)和心动力图(CDG)特征,开发一种多层感知器(MLP)算法,实现CMD的无创早期检测。方法收集82例CMD患者和107例健康对照者的心电图,并将其合成VCG。提取VCG的ST-T片段,并将其输入确定性学习算法以开发CDG。从VCG的ST-T片段和CDG中提取时间异质性指数、空间异质性指数,样本熵、近似熵和复杂性指数,分别称为基于STT和CDG的特征。通过顺序后向选择算法从基于CDG、基于STT和组合特征(即所有特征)中确定最有效的特征子集,分别作为用改进的麻雀搜索算法优化的基于CDG-、STT-和CDG-STT-的MLP模型的输入。最后,通过五次交叉验证评估了相应模型的分类能力,并在测试数据集上进行了测试,以验证最优模型。结果在验证数据集上,基于CDG-STT的MLP模型的评估指标显著高于基于CDG-和STT的模型,其准确性、敏感性、特异性、F1评分和AUC在测试数据集上分别为0.904、0.925、0.870、0.870和0.897。结论基于VCG和CDG特征的MLP模型对CMD具有较高的识别效率。基于CDG-STT的MLP模型可以为CMD的非侵入性检测提供潜在的计算机辅助工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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