Effects of Stroke Localization on Nonlinear Indexes of HRV G D'Addio 1 , A Accardo 2 , G Corbi 1 , G Russo 1 , GD Pinna 1 , N Ferrara 1 , F Rengo 1 1 S Maugeri Foundation, Italy 2 DEEI, University of Trieste, Italy Abstract To evaluate the relationship between lesion’s severity and nonlinear indexes of HRV, 20 first-ever stroke subjects and 10 healthy subjects were studied. All patients, divided in two groups according to presence of single or multiple medium cerebral artery lesion, underwent to a 24-hour Holter ECG recording. All RR time series were analyzed by Poincarè Plot, fractal dimension, power-law behaviour, spectral and time- domain techniques. A direct relationship between increasing lesion’s severity and progressive collapsing of PPlots and FD index was observed, while lower significance were found for beta exponent, spectral and time-domain parameters. These results suggest that PPlots and FD analysis contains relevant information related to different HRV dynamics in normal and stroke subjects with different lesion’s severity. 1. Introduction Cerebrovascular diseases represent one of the main cause of death and disability in western countries. An impaired cardiovascular autonomic regulation has been described in stroke patients (SP) with dysfunction, that often complicating the clinical course of these pathology. It has been hypothesized that these abnormalities are mediated by the central nervous system as a result of the cerebrovascular event, whereas the mechanism of this phenomenon is not fully understood [1]. The analysis of heart rate variability is a well recognized non-invasive tool to investigate the cardiovascular autonomic control but only limited data are available on the autonomic imbalance assessment of stroke patients by heart rate variability changes after a prior single stroke, using time- and frequency-domain linear methods [2]. Recently non-linear analysis of heart rate variability has been suggested to provide more valuable information for physiological interpretation of heart rate fluctuation and for risk assessment [3]. Poincarè's plots analysis and measures of the fractal behaviour of beat-to-beat time series are some of the few nonlinear methods tested in clinical settings in the last years. Poincarè's plots (PPlots) allow to detect patterns resulting from non-linear processes that may not be observable by time- and frequency-domain analysis [4]. Several Poincarè plots analysis' methods have been proposed in literature, but it has clearly been shown that most of them bring back to existing linear measure of heart rate variability [5] and only nongeometric techniques, such as scanning parameters [6], allow to detect patterns resulting from non-linear processes that cannot be measured by time- and frequency-domain analysis. Among non-linear methods proposed to measure the fractal behaviour of the HRV signal, that based on the beta exponent of the 1/f-like relationship, starting from the spectral power [7], and that based on the fractal dimension (FD) have gained wide interest in the last years. The latter has traditionally been approached following the chaos-theory, with the aim of modelling the attractor extracted from HRV sequences, and the FD parameter has usually been estimated from the slope of the 1/f relationship. However, the FD can also be directly extracted from HRV sequences using different methods. In this paper we followed an approach based on the use of the FD estimated by Higuchi algorithm [8]. This method allows a better fractal estimation, eliminating the errors due to the indirect estimation of FD from spectral power. The aim of the present paper was to evaluate the relationship between lesion’s severity and nonlinear indexes of HRV in stroke patients, comparing these results with those of traditional time- and frequency- domain linear HRV parameters. 2. Study population The study population consisted of 20 patients consecutively admitted to Neurology Rehabilitation Division of “Salvatore Maugeri” Foundation. All enrolled ISSN 0276-6547 621 Computers in Cardiology 2006;33:621-624.