Electrocardiogram ST-Segment Morphology Variability Analysis Base on Correlation Coefficient Entropy and Inverse Correlation Coefficient Entropy

Wei, Ming; Song, Jin zhong; Yan, Hong

doi:10.4028/www.scientific.net/amm.195-196.550

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Therefore, these ischemia-related changes increase the complexity of beat-to-beat ST-T segments, leading to a higher SampEn . It is demonstrated that ST segments induced by myocardial ischemia have relatively large morphological variability ( Wei et al, 2012 ). The average SampEn obtained from ischemic ST segments of ECGs was found to be higher than that of healthy subjects ( Rabbani et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

“…For detecting myocardial ischemia, various entropies calculated from HRV ( Udhayakumar et al, 2019 ), ST segments ( Rabbani et al, 2011 ; Wei et al, 2012 ) or filtered 12-lead ECGs ( Liu et al, 2019 ) have been used. To quantitatively evaluate the complexity of physiological time-series and diagnose diseases, Richman and Moorman refined the approximate entropy algorithm and introduced sample entropy ( SampEn ) by excluding the self-matching of templates’ data length ( Richman and Moorman, 2000 ).…”

Section: Methodsmentioning

confidence: 99%

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Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

et al. 2022

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Background: 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.Methods: 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 (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn 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 (SI, THI, and SHI, where SI is the SampEn 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.Results: The ECG + VCG model with three features (SI,THI, and SHI) 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.Conclusion: 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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

et al. 2022

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Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

Zhao

Gong

et al. 2023

MBE

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<abstract> <sec><title>Purpose</title>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. </sec> <sec><title>Aim</title>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. </sec> <sec><title>Methods</title>Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (<italic>SampEn</italic>), approximate entropy (<italic>ApEn</italic>), and complexity index (<italic>CI</italic>) 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 <italic>SampEn</italic>-based, <italic>ApEn</italic>-based, and <italic>CI</italic>-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. </sec> <sec><title>Results</title><italic>ApEn-</italic>based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, <italic>ApEn</italic>-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. </sec> <sec><title>Conclusions</title>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. </sec> </abstract>

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Electrocardiogram ST-Segment Morphology Variability Analysis Base on Correlation Coefficient Entropy and Inverse Correlation Coefficient Entropy

Cited by 2 publications

References 12 publications

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

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