Abstract
Sudden cardiac arrest (CA) is a common cause of death, and cardiopulmonary resuscitation (CPR) can improve the survival rate of CA patients. However, the in-depth study of the pathophysiological mechanism of patients during CPR is limited, and the characterization of the dynamic structure behind the electrical activities of the cardiovascular system from the perspective of nonlinear time series analysis is still lacking. This study used recurrence quantitative analysis (RQA) to quantify changes in the cardiovascular system during CPR and analyze its pathophysiological mechanisms. In artificially constructed porcine CA models, data were divided into four periods: Baseline, ventricular fibrillation (VF), CPR, and Recovery of spontaneous circulation (ROSC). RQA parameters of electrocardiogram (ECG) were analyzed to compare the changes in cardiovascular system dynamics in four periods. The RR, ENTR, and TT of ECG were significantly higher than those of VF and CPR at Baseline and ROSC, indicating that the period and stability of electrical activity of the cardiovascular system were significantly reduced under pathological conditions. The RQA is valid in cardiovascular system analysis in CA patients. This may be useful for future research on the diagnosis and prediction of CA.
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This study was supported by National Key Research and Development Program (2020YFC1512701), Technical Innovation Guidance Plan of Shandong Province, and Youth Interdisciplinary Scientific Innovation Project of Shandong University.
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Chen, S. et al. (2022). Recurrence Quantification Analysis of Cardiovascular System During Cardiopulmonary Resuscitation. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_68
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DOI: https://doi.org/10.1007/978-3-031-20233-9_68
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