摘要
为寻找抑郁症患者早期诊断指标,采集了5例未经抗抑郁药物干预的抑郁症患者和6例正常被试者静息状态下的脑电数据,采用样本熵算法,研究抑郁症患者脑电时间序列复杂度。结果表明,2组数据睁眼状态都比闭眼状态的样本熵数值大,说明睁眼状态的时间序列复杂度要高于闭眼状态。抑郁组的睁眼状态和闭眼状态样本熵数值分别比正常组大,说明抑郁组睁眼和闭眼状态时间序列复杂度比正常组高。研究表明,样本熵的处理方法能有效区分抑郁症患者和正常对照组,样本熵可作为判别抑郁症的生物学指标,为抑郁症的早期诊断提供一种辅助方法。
In order to find indicators for early diagnosis of depression patients,the resting EEG data is collected from 5depression patients and 6normal subjects.Using sample entropy,time series complexity is studied on patients with depression.The results shows that the sample entropy of eyes opening is higher than eyes closed in two groups.It indicates that the time series of eye opening has a higher degree of complexity than eyes closed.The depression group has a higher sample entropy than the normal group in two stages,the time series complexity of the depression group is higher than the normal group.The sample entropy can effectively distinguish depression patients with normal people.So it can be used for aiding early diagnosis of depression.
出处
《桂林电子科技大学学报》
2014年第5期382-385,共4页
Journal of Guilin University of Electronic Technology
基金
广西自然科学基金(2011GXNSFA18183)
关键词
样本熵
抑郁症
静息脑电数据
sample entropy
depression
resting EEG data
