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生理信号属于非稳态的时变信号.因此,非线性分析方法能更好地揭示其特性与机理.已有研究表明,由复杂自调节系统产生的生理信号具有分形结构.提出一种新的多重分形复杂度检测方法——质量指数谱分析,着重刻画谱曲线的整体弯曲程度,揭示分形结构复杂性.方法描述了该谱曲线上所有相邻点对应分形维数差异的非线性叠加,解决原有参数无法充分反映信号分形结构全部子集信息的不足.用确定性分形系统Cantor集进行检验,完全可以区分不同复杂程度的混沌序列,并不受信号非平稳性及噪声干扰影响.通过对人体心率变异性(HRV)信号和睡眠脑电(sleepEEG)信号统计分析,可有效判别处于不同生理和病理状态的人群,在运算速度和准确性上均比传统参数有一定提高.该研究能够为临床应用提供有价值的信息.
Physiological signals belong to unsteady time-varying signals.Therefore, the nonlinear analysis method can better reveal its characteristics and mechanism.Researches have shown that the physiological signals generated by the complex self-regulating system have a fractal structure.A new The method of multifractal complexity detection - mass index spectrum analysis emphasizes the overall curve degree of the spectral curve and reveals the complexity of the fractal structure.Methods describes the nonlinear superposition of the differences of fractal dimensions of all the adjacent points on the spectral curve The original parameters can not fully reflect the inadequacy of the information of all subsets of the signal fractal structure.Using the Cantor set of deterministic fractal system to test, it can completely distinguish the chaotic sequences of different complexity from the signal nonstationarity and noise interference. Human heart rate variability (HRV) signal and sleepEEG signal statistical analysis, can effectively distinguish in different physiological and pathological populations, in the operation speed and accuracy than the traditional parameters have increased.This study can be Clinical applications provide valuable information.