Composite Multivariate Multiscale Entropy Analysis

• Main Authors: Liu, Wen-Hao, 劉文豪
• Other Authors: Shann, Jyh-Jiun
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/8byt6x

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 104 === Entropy is used for measuring signal complexity. In order to understand the structural complexity of a signal, analysis of the complexity changes in multiple scales is the trend of entropy study. Costa, M. proposed the multiscale entropy analysis in time domain, but these low-frequency scales are unable to reveal the high-frequency information of the signal. The multiscale entropy analysis in frequency domain is soon developed. Use Empirical Mode Decomposition proposed by Norden E. Huang, extracting Intrinsic Mode Functions from the signal in different frequency band. Therefore, frequency scales are obtained by the cumulative sums of the intrinsic mode functions. But without objective measurements, these scales cannot be considered as explicitly defined. Because of the transient nature of intrinsic mode function, the Instantaneous Frequency inferred Power Spectral Density is used. We capture the features of scales by extracting the position and spread parameter of the distribution of scale signals in the PSD. The signal of each scale is described with three parameters: the position and the spread of the distribution, the complexity of the signal. In this research, we use frequency modulate signal, noise and noisy sinusoidal signal as examples to distinguish the trend of each signal. Also we use two status of Steady State Visually Evoked Potential signal: under 35Hz flickering stimuli and rest, then observe the trend of each status. We do notice the entropy decrease in the trend of stimuli frequency band, due to the potential response under stimulated state. Most important of all, this research provides an objective basis for multiscale entropy comparison between signals by capturing scale features of distribution in IF inferred PSD instead of using index. As a result, the multiscale entropy analysis of frequency domain tends to be completed.