Key result
A support vector machine classifier using multivariate recurrence networks achieved 94.1% and 95.58% accuracy in distinguishing healthy subjects from those with myocardial infarction on two datasets.
Why the study?
To investigate the effects of age and cardiovascular disease on the cardiac system using multivariate recurrence networks with multiple scale factors from ECG signals.
Does a multivariate recurrence network approach using ECG signals accurately detect myocardial infarction compared to healthy heartbeat dynamics?
Does a multivariate recurrence network approach using ECG signals accurately detect myocardial infarction compared to healthy heartbeat dynamics?
A novel computational approach using multivariate recurrence networks and SVM on ECG signals achieved high accuracy (>94%) in distinguishing healthy subjects from those with myocardial infarction.
Supports ECG-based MI screening tools; extends multivariate recurrence networks for cardiac signal classification in RCTs.
The electrocardiogram (ECG) is one of the physiological signals applied in medical clinics to determine health status. The physiological complexity of the cardiac system is related to age, disease, etc. For the investigation of the effects of age and cardiovascular disease on the cardiac system, we then construct multivariate recurrence networks with multiple scale factors from multivariate time series. We propose a new concept of cross-clustering coefficient entropy to construct a weighted network, and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties. The obtained results suggest that these two network measures show distinct changes between different subjects. This is because, with aging or cardiovascular disease, a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system. Consequently, the complexity of the cardiac system is reduced. After that, the support vector machine (SVM) classifier is adopted to evaluate the performance of the proposed approach. Accuracy of 94.1% and 95.58% between healthy and myocardial infarction is achieved on two datasets. Therefore, this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.
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Ma et al. (2023) studied Myocardial infarction. Multivariate recurrence networks with multiple scale factors and SVM classifier was evaluated on Accuracy of distinguishing healthy subjects from those with myocardial infarction. A support vector machine classifier using multivariate recurrence networks achieved 94.1% and 95.58% accuracy in distinguishing healthy subjects from those with myocardial infarction on two datasets.
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