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摘要: 模糊熵（Fuzzy entropy，FuzzyEn）是衡量时间序列在维数变化时产生新模式的概率，反映时间序列复杂性和无规则程度的参数指标.本文针对传统模糊熵算法只针对时间信号序列进行总体分析，忽略了瞬时信号变化的问题，提出了一种改进模糊熵的算法.算法将指数函数的宽度进行了优化设置，设置为0.15倍一阶差分时间序列的标准差，以此保证充分提取时间序列瞬时复杂性特征.与传统模糊熵相比，改进模糊熵包含更多时间模式信息.基于改进模糊熵结合锁相位算法，分析孤独症儿童脑电信号（Electroencephalogram，EEG）复杂性与同步性，结果表明：孤独症（Autism spectrum disorders，ASD）前颞叶的脑电信号同步性下降、复杂性降低，具有显著性差异（P < 0.05）.Abstract: Fuzzy entropy (FuzzyEn) is used to measure the probability of a new model when the dimension of the time series changes, and to represent the complexity and irregularity of time series. Traditional FuzzyEn only analyzes the signal sequence in a period of time, ignoring the signal changes in each time series. Focusing on this problem, we proposed an improved FuzzyEn algorithm, which sets the width of the exponential function to 0.15 times the standard deviation of the first-order difference time series. Compared with the traditional FuzzyEn, the improved FuzzyEn contains more time pattern information. Electroencephalogram (EEG) signals of autism spectrum disorders (ASD) are analyzed based on the improved FuzzyEn combined with phase locking value. The results showed that synchronization in the anterior temporal lobe of the brain decreases in ASD and the complexity is reduced (P < 0.05).1) 本文责任编委 张学工
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表 1 两种特征获得的识别率
Table 1 The classification accuracy obtained by different features
特征向量 改进模糊熵 传统模糊熵 正确率 86.67 46.67 测试运行时间 0.39 3.68 
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