阿尔茨海默病患者自发脑电信号子波熵的研究

Wavelet Entropy Analysis of Spontaneous EEG Signals in Alzheimer's Disease

子波熵是衡量信号复杂程度的指标,本文采用连续子变换的方法对轻、中、重度阿尔茨海默病（AD）患者及正常对照老年人的脑电（EEG）信号进行子波分析,根据子波系数计算EEG信号的子波功率谱分布,提取描述EEG信号复杂程度的定量指标——子波熵。对轻、中、重度AD患者和正常对照的自发状态下EEG信号的子波熵值进行比较,并将子波熵值与MMSE进行相关性分析。结果显示,轻、中、重度AD组和正常对照组之间EEG信号的子波熵存在显著差异（P〈0.01）。组间比较显示轻、中、重度AD患者EEG信号的子波熵均低于正常对照,差异具有统计学意义（P〈0.05）。这与AD患者EEG信号的功率谱分布单一有关。进一步研究表明EEG信号的子波熵与其MMSE评分均存在显著相关（r=0.601-0.799,P〈0.01）。子波熵可以作为描述EEG信号复杂程度的定量指标,子波熵值有可能成为AD诊断和病情评估的电生理指标。

Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram （EEG） signals of mild, moderate and severe Alzheimer＇s disease （AD） patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls （P〈0.01）. Group comparisons showed that wavelet entropy for mild, moderate, severe AD pa- tients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant （P〈0.05）. Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated （r= 0. 601-0. 799, P〈0.01）. Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.