阿尔茨海默病患者自发脑电信号子波熵的研究

发布时间：2018-03-04 19:32

本文选题：阿尔茨海默病　切入点：脑电图　出处：《生物医学工程学杂志》2014年04期 　论文类型：期刊论文

【摘要】：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(P0.01)。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(P0.05)。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,P0.01)。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。
[Abstract]:Wavelet entropy is an index to measure the signal complexity. In this paper, wavelet analysis of EEG (EGG) signals in patients with mild, moderate and severe Alzheimer's disease (ADD) and normal controls was carried out by means of continuous subtransform. The wavelet power spectrum distribution of EEG signal was calculated according to wavelet coefficients, and wavelet entropy, a quantitative index describing the complexity of EEG signal, was extracted. The wavelet entropy values of EEG signal in spontaneous state of light, moderate and severe AD patients and normal controls were compared. The correlation between wavelet entropy and MMSE was analyzed. The results showed that there were significant differences in wavelet entropy of EEG signal between mild, moderate and severe AD groups and normal controls. The wavelet entropy of EEG signal in patients with severe AD was lower than that in normal controls. The difference is statistically significant (P 0.05), which is related to the single distribution of power spectrum of EEG signal in AD patients. Further study shows that there is a significant correlation between the wavelet entropy of EEG signal and its MMSE score. The wavelet entropy can be used to describe the complexity of EEG signal. Quantitative indicators of degree, Wavelet entropy may be an electrophysiological index for diagnosis and assessment of AD.
【作者单位】：天津市人民医院神经内科;天津医科大学总医院神经内科;
【基金】：天津市卫生局科技基金资助项目(2011KY21) 天津市自然科学基金资助项目(14JCYBJC27000)
【分类号】：R749.16

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