Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/90494
標題: Studying sign and magnitude series of electroencephalography by using fractal analysis
應用碎形分析來研究腦波信號的符號與幅值序列
作者: Yun-Jie Tsai
蔡雲傑
關鍵字: 睡眠
腦波
碎形維度
幅值序列
符號序列
波動
sleeping
EEG
fractal dimension
magnitude series
sign seies
volatility
引用: [1] A. Rechtschaffen & A. Kales, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, U.S. Department of Health, National Institutes of Publication No. 204(1968) [2] J. W. Kantelhardt, Y. Ashkenazy, Pl. Ch. Ivanov, A. Bunde, S. Havlin, T. Penzel, J .Peter, and H. E. Stanley, Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments, Phys. Rev. E 65(2002), 051908 [3]Y. Ashkenazy, Pl. Ch. Ivanov, S. Havlin, C.K. Peng, A. L. Goldberger, and H. E. Stanley, Magnitude and Sign Correlations in Heartbeat Fluctuations, PhysRevLett.86.1-900(2000) [4] http://www.physionet.org/ [5] S.-S. Liaw and F.-Y. Chiu, Fractal dimensions of time sequences, Physica A388, 3100(2009) [6] S.-S. Liaw, F.-Y. Chiu, C.-Y.Wang, and Y.-H. Shiau, Fractal analysis of stock index and electrocardiograph, Chinese Journal of Physics 48, 814(2010) [7] C.K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, A.L. Goldberger, Mosaic organization of DNA nucleotidesPhys. Rev. E 49 (1994) 1685. [8]C.S Hung, Characterizing sleep by fractal analysis on electroencephalograph, master thesis of NCHU(2014) [9] K. Yamasaki, L. Muchnik, S.Havlin, A. Bunde, and H. E. Stanley, Scaling and memory in volatility return intervals in financial markets, Proc. Natl. Acad. Sci. USA 102 (2005) 9424. [10] B.B. Mandelbrot, The Variation of Certain Speculative Prices, The Journal of Business 36, No. 4, (1963), 394-419 [11]C.CHEN and L.WANG,Dual Fractal Dimension and Long-Range Correlation of Chinese Stock Prices J. Phys. Soc. Jpn 81 034801(2012) [12]W.S. Li, S.S. Liaw, Abnormal statistical properties of stock indexes during a financial crash, Physica A 422 73(2015)
摘要: 健康的睡眠會經過三種不一樣的狀態:淺眠、深眠與快速動眼狀態。在這篇文章中,我們把EEG原始數據分解成符號序列與幅值序列,然後利用mIRMD與DFA法來分析它們的特性。我們展示符號序列與幅值序列在各個睡眠狀態的維度趨勢,並且區分了睡眠期的清醒與非睡眠期的清醒狀態,我們更進一步把幅值序列再分解成幅值的符號序列,並且從比較分析生醫與金融數據中發現了一些有趣的現象。藉由研究腦波波動的復發間隔分析,我們能夠在特定睡眠狀態下分辨出哪些人是健康的以及那些人是擁有輕微睡眠障礙的個體。
URI: http://hdl.handle.net/11455/90494
文章公開時間: 2015-07-10
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