Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

Abstract

The electrocardiogram (ECG) is one of the physiological signals applied in medical clinics to determine the 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 contribute 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. The accuracy of 94.1% and 95.58% between healthy and myocardial infarction 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.