Article ID Journal Published Year Pages File Type
562566 Biomedical Signal Processing and Control 2014 7 Pages PDF
Abstract

•Heart rate variability (HRV) is a useful measure of autonomic function.•Frequency domain analysis of HRV in 68patients with epilepsy was performed.•Two-parametric and two non-parametric spectral methods were employed.•Epilepsy-related abnormalities of HRV were not consistent in all four methods.•Parametric methods seemed to be more appropriate for this type of analysis.

PurposeSpectral analysis of heart rate variability (HRV) constitutes a useful tool for the evaluation of autonomic function. However, it is difficult to compare the published data because different mathematical approaches for the calculation of the frequency bands are applied. Our aim was to compare the HRV frequency domain parameters obtained by application of 2 parametric and 2 non-parametric spectral methods in a group of patients with chronic epilepsy.MethodsSixty-eight patients and 69 healthy controls underwent a 5-min recording of RR signal, which was analyzed off-line in time and in frequency domains.ResultsThe time domain parameters – variation RR ratio, standard deviation of normal-to-normal RR and coefficient of variation – were significantly lower in patients than in controls. In spectral analysis of the patient group deviation toward opposite directions of Low Frequency band (p = 0.034) and Total Power (p = 0.013) measures was detected depending on the method used. The results of Burg's and Yule-Walker's parametric methods fitted best to those of time domain estimates for both control and patient groups.ConclusionsEpilepsy-related abnormalities of HRV were disclosed by time as well as by frequency domain analysis. In the present setting, the parametric methods proved to be superior to the non-parametric ones in matching time domain parameters of patients and healthy subjects and at the same time in detecting abnormalities of the frequency domain measures of patients with epilepsy.

Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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