Nonlinear, Time-Varying and Frequency-Selective Analysis During the Orthostatic Challenge in Patients with Vasovagal Syncope
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In this study, dynamics of the response to an orthostatic challenge was investigated by the combination of an Empirical Mode Decomposition based method and the time varying nonlinear Point Prediction Error algorithm, used for the analysis of heart rate variability (HRV) calculated from time series of beat-to-beat intervals (BBI), and systolic blood pressure (SBP) in patients with vasovagal syncope (VVS) and healthy subjects to provide more information on the mechanisms leading to changes of the autonomic nervous system. This study included 7 young female patients with vasovagal syncope and 7 age-matched female controls. The subjects were enrolled in a head-up tilt (HUT) test, breathing normally, including 5 min of supine position, 15 s of head-up tilting and 18 min of 70 %26#x0024;%26#x0024;^\circ %26#x0024;%26#x0024; orthostatic phase. This time-variant, frequency-selective nonlinear analysis seemed to be suitable to distinguish between patients and healthy subjects even in the transition phase. The results allowed us to differentiate the response of patients with VVS and healthy subjects, mainly in the HF band of both the BBI and the SBP time series. © 2020, Springer Nature Switzerland AG.
Heart rate variability; Nonlinear analysis; Signal-adaptive decomposition; Time-variant analysis; Vasovagal syncope Biomedical engineering; Biophysics; Blood pressure; Heart; Nonlinear analysis; Signal processing; Time series; Adaptive decomposition; Autonomic nervous system; Empirical Mode Decomposition; Frequency selective analysis; Heart rate variability; Systolic blood pressure(SBP); Time variant; Vasovagal syncope; Time series analysis
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