Objective Multiscale entropy (MSE) is a recently proposed entropy-based index of physiological complexity, evaluating signals at multiple temporal scales. with applied at temporal scales might serve as a complementary approach for characterizing and understanding abnormal cortical dynamics in AD. consecutive data points are similar to each other (within given tolerance + 1) in the data set (is the length of the time series, is the length of the sequence to be compared and is the effective filter for measuring Rabbit Polyclonal to GFM2 consistency of time series. Considering for any 173550-33-9 manufacture coarse-grained EEG time-series {(C | < C + 1) (C is a vector of members time series of (C | denotes the distance (Euclidian distance actually) between points and in the space of dimension (for details of the SampEn algorithm see Richman and Moorman, 2000). 173550-33-9 manufacture Various theoretical and clinical applications have shown that = 1 or 2, and = 0.1C0.25 of the standard deviation of the original sequence provides good statistical validity for SampEn (Richman and Moorman, 2000). For the present analyses, the calculation of MSE was carried out using self-produced software developed with Mathematica 5.2 (Wolfram Research, Inc.), and we used a time series of length = 12000 173550-33-9 manufacture (i.e. 60-sec 200 Hz) with = 2, = 0.2 and 173550-33-9 manufacture SF = 1 C 20, which are values that have been successfully applied in our previous work (Takahashi et al., in press; Takahashi et al., 2009). 2.4. Power analysis In addition to MSE analysis, we performed power analysis as a comparative conventional EEG measurement using a computer program specifically designed for EEG, BIMUTAS II (Kissei-Comtec). A Hanning window was applied to each artifact-free 2.56-s epoch (sampling rate 200 Hz), and the spectral density was calculated using a fast Fourier transform (FFT). From the consecutive 60-s epochs which were used for MSE analyses, a total of 23 artifact-free epochs were selected to calculate absolute EEG power. Then the frequency spectrum was divided into frequency bands of delta 173550-33-9 manufacture (2C6 Hz), theta (6C8 Hz), alpha (8C13 Hz), beta (13C30 Hz) and gamma (30C40 Hz). For each frequency band, we then calculated a measure of relative power change (power in each frequency divided by total power across all frequency bands) for statistical analyses. 2.6. Statistical analysis Statistical analyses were carried out using SPSS (Windows version 17; SPSS Japan Inc., Tokyo, Japan). SampEn values for each SF were found to have a skewed distribution and were therefore log-transformed to achieve a normal distribution. For MSE analysis, repeated measures analysis of variance (ANOVA), with group (AD vs. HC) as a between-subject factor, and hemisphere (left vs. right) and SF (: 20 scales) as within-subject factors, were used to test differences in MSE analysis for each paired electrode site. For midline electrode sites, repeated measures ANOVA, with group (AD vs. HC) as a between-subject factor, and SF (: 20 scales) as within-subject factors, were used to test for group differences in MSE analysis. In the case of significant group-by-SF interaction, post-hoc independent = 8) with low MMSE scores (MMSE score 15), and similarly performed ANOVA and post-hoc independent = 0.006], P3/4 [= 0.004] and O1/2 [= 0.0013], and a trend group-by-SF interaction in F3/4 [= 0.016], C3/4 [= 0.016] and T5/6 [= 0.012] was identified for each paired electrode sites, but not in F7/8 [= 0.05]. For intermediate electrode sites, a significant and a trend group-by-SF interaction was identified in both Fz [F(19,589) = 5.8, P = 0.007] and Pz [F(19,589) = 5.0, P = 0.012]. Post-hoc = 0.00002], F3/4 [= 0.000004], F7/8 [= 0.002], C3/4 [= 0.00002], P3/4 [= 0.00003], T5/6 [= 0.0001] and O1/2 [= 0.0013]). For intermediate electrode sites, both Fz [F(19, 456) = 14.2, P = 0.00001].