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Fractal Behaviour of Heart Rate Variability Reflects Severity in Stroke Patients

机译:心率变异性的分形行为反映中风患者的严重程度

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Non-linear parameters obtained from heart rate variability (HRV) analysis has recently been recognized to provide valuable information for physiological interpretation of heart rate fluctuation. Among the numerous nonlinear parameters related to the fractal behaviour of the HRV signal, two classes have gained wide interest in the last years: the beta exponent based on the 1/f-like relationship, starting from the spectral power, and that based on fractal dimension. In order to evaluate the relationship between lesion's severity and fractal behaviour, 20 first-ever stroke subjects and 10 healthy subjects were studied. Patients were divided in two groups according to single or multiple medium cerebral artery lesions. All subjects underwent 24-hour Holter recording analysed by fractal and 1/f-like techniques. Differently from methods usually used in literature to evaluate the fractal dimension (FD), in this work the FD was extracted by using the Higuchi's algorithm that permits to calculate the parameter directly from the HRV sequences in the time domain. Results show that fractal analysis contains relevant information related to different HRV dynamics that permits to separate normal subjects from stroke patients. FD is also able to distinguish between normal and stroke subjects with different lesion's severity.
机译:从心率变异性(HRV)分析获得的非线性参数最近被认为可以为生理解释心率波动提供有价值的信息。在与HRV信​​号的分形行为有关的众多非线性参数中,最近几年有两类引起了广泛关注:基于1 / f关系的β指数(从频谱功率开始)和基于分形的β指数尺寸。为了评估病变严重程度与分形行为之间的关系,研究了20名首次卒中患者和10名健康受试者。根据单个或多个脑中动脉病变将患者分为两组。通过分形和1 / f样技术对所有受试者进行24小时动态心电图记录。与文献中通常使用的评估分形维数(FD)的方法不同,在这项工作中,FD是使用Higuchi算法提取的,该算法允许直接从时域的HRV序列计算参数。结果表明,分形分析包含与不同HRV动态相关的相关信息,可将正常受试者与中风患者分开。 FD还能够区分具有不同病变严重程度的正常受试者和中风受试者。

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