Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis

摘要

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyophaty patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyophaties were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and T time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopaties, obtaining an accuracy of 85.7%.