臨床醫學常使用腦波來檢查中樞神經生理疾病。如當人清醒時，用外部閃光刺激眼睛所產生之腦波視覺誘發電位來評估視神經通路是否有異常，如視神經炎、視神經萎縮等等；當睡眠時，用睡眠腦波分期可作為客觀評估患者睡眠障礙之依據，如慢性失眠、呼吸中止症等等。目前，市面上的腦波分析軟體還未有此整合性的功能，所以本研究希望開發一整合系統，可以具有誘發電位分析及睡眠腦波分期的功能以便進行較寬廣以腦波為基礎之學術研究。
本研究所建立以LabVIEW為基礎之腦波分析軟體系統可分為兩個子系統：(1)視覺誘發電位分析子系統：本子系統之輸入訊號為腦波與視覺刺激訊號，可利用本團隊建構之棋格翻轉視覺刺激腦波擷取系統平台所做的實驗數據得到。子系統首先將腦波訊號以數位濾波濾除雜訊後，依研究需要選擇小波轉換(wavelet)及獨立成份分析(Independent Component Analysis, ICA)做更進一步的雜訊濾波，再經累加平均法提取出視覺誘發電位，最後以ERP Image方式將結果呈現出來。(2)睡眠腦波分期子系統：本子系統之輸入訊號是單通道腦波，它可透過可攜式多項生理檢查記錄儀或本團隊自製之腦波擷取模組取得，該訊號先使用小波去除雜訊，再由非線性分析之近似熵及樣本熵方法作特徵粹取與規則分類法，最後得到初步的睡眠腦波分期結果。
為了驗證視覺誘發電位分析子系統的可行性，設計一套耳穴電刺激與視覺誘發電位分析實驗。該實驗利用NeuroScan儀器收集腦波資料並分別由腦波分析軟體(EEGLAB)及本系統軟體來做視覺誘發電位分析，發現由EEGLAB所得到的誘發電位波形與本系統一致，最後再用統計方法判斷其相關性。初步結果發現由EEGLAB所提取出的視覺誘發電位之潛伏期特徵在刺激頻率是1 Hz、3 Hz、5 Hz時有統計意義(p<0.05)，但本系統其統計結果只有3 Hz及5 Hz有統計意義。由此推論，可能EEGLAB程式與自行撰寫之程式，在內部上設定之參數有所不同所造成。其次，在睡眠腦波分期子系統驗證上，採用MIT-BIH資料庫腦波，先以小波轉換有效地去除雜訊，再求取各睡眠階段之近似熵及樣本熵，經樣本熵及近似熵處理的腦波訊號能夠準確的反應睡眠各期的變化特徵，發現清醒期時其值最大，第一期及第二期逐漸降低，第三期到第四期時達到最低值，直到進入REM期後逐漸回升至第一期與第二期之間。目前REM期介於第一期左右，第三期與第四期過於接近外，所以將第三期與第四期歸類為慢波睡眠(SWS)，其餘各分期結果較為明顯，目前的初步結果與MIT-BIH資料庫專家綜合了生理參數所判定的結果達到62%。REM期無法確定之問題，未來將利用較前瞻之分類技術解決。

In clinical studies, EEG is frequently used to diagnose psychological diseases. When people are awake, visual evoked potential induced by visual stimulation is commonly used to assess whether there are lesions on the visual nervous system, such as optic neuritis. While during sleep, the sleep EEG stage is often used to assess whether patients has sleep disorders, such as insomnia, apnea syndrome, atrophy etc. However, integrated EEG analysis software is not yet available on the market. Therefore, the focus of the study is to develop such on integrated system that can provide a broad EEG-based academic research.
This study has established a LabVIEW-based EEG analysis system. The system is divided into two subsystems: (1) Visual evoked potential analysis system: its internal functions include the realization of EEG through spectral analysis, filter selection and the use of independent component analysis (ICA). Moreover, wavelet analysis is utilized to carry out noise filtering and ultimately the ERP image and pattern reversal-visual evoked potential (PR-VEP) as its results; (2) Sleep EEG staging sub-system: The system a single channel EEG from a PSG device or from the EEG module developed by the team. Signal correction is done by using wavelet to remove noise signals. Afterwards, data can be processed using approximate entropy or sample entropy before passing through a rule-based classification to determine the different sleep stages.
In order to verify the feasibility of the visual evoked potential analysis system, an auricular electrical stimulation was designed to perform experimental analysis. EEG data were collected from a commercial device, NeuroScan, and then the analysis of evoked potentials was done using the EEG software, EEGLAB and the system’s software. Preliminary results showed that the extracted latency of the visual evoked potential characteristics with stimulus frequencies of 1 Hz, 3Hz and 5 Hz produced by EEGLAB has statistical significance (p<0.05). As a corollary, some differences may occur between EEGLAB and the developed program due to some internal setup differences. Moreover, for the verification of the sleep stage classification subsystem, data were taken from the MIT-BIH database. First, wavelet transform was used to effectively remove noise. Second, the approximate entropy and sample entropy was used to compute the sleep stage to determine the characteristic changes of the EEG signals. It showed that wake has the largest value and a gradual decrease as sleep enters into stage 1 and 2. When sleep stage 3 and 4 was reached the lowest entropy values were computed. But as sleep enters into REM the value falls between stage 1 and stage 2. Currently REM value is situated around stage 1, while the values of stage 3 and 4 are so close that the two can be combined and renamed as slow wave sleep (SWS). However sleep staging using this method still has room for improvement. As of the moment preliminary results when compared with the expert diagnosis of MIT-BIH database reached an accuracy of 62% but REM cannot be determined clearly. In the future, a more advanced classification system would be applied to do the classification.

摘 要 ...................................................................................................................................... II
Abstract ................................................................................................................................... III
致謝 .......................................................................................................................................... V
圖目錄 ................................................................................................................................... VIII
表目錄 ..................................................................................................................................... XI
第一章 緒論 ................................................................................................................. 1
1.1 研究背景與動機 ............................................................................................. 1
1.2 腦波分析相關技術之回顧 ............................................................................. 2
1.2.1 誘發電位提取之研究概況 ............................................................................. 2
1.2.2 自動睡眠分期之研究概況 ............................................................................. 3
1.3 研究目的 ......................................................................................................... 7
1.4 章節內容概要 ................................................................................................. 8
第二章 相關文獻回顧及基本理論 ............................................................................. 9
2.1 腦的生理結構 ................................................................................................. 9
2.2 腦波的產生 ................................................................................................... 10
2.3 腦波的分類 ................................................................................................... 12
2.3.1 誘發電位腦波 ............................................................................................... 12
2.3.2 睡眠腦波 ....................................................................................................... 15
2.4 腦波電極紀錄 ............................................................................................... 20
2.5 腦波干擾問題 ............................................................................................... 21
2.6 腦波分析方法原理 ....................................................................................... 21
2.6.1 累加平均法 ................................................................................................... 22
2.6.2 小波理論 ....................................................................................................... 23
2.6.3 獨立成份分析 ............................................................................................... 32
2.6.4 近似熵 ........................................................................................................... 34
2.6.5 樣本熵 ........................................................................................................... 37
第三章 研究方法 ....................................................................................................... 40
3.1 系統整體架構 ............................................................................................... 40
3.2 使用材料 ....................................................................................................... 41
3.2.1 量測儀器 ....................................................................................................... 41
3.3 實驗設計 ....................................................................................................... 44
3.3.1 頭皮電極處理 ............................................................................................... 44
3.3.2 PR-VEP實驗設計 ........................................................................................ 45
3.3.3 睡眠實驗設計 ............................................................................................... 46
3.4 軟體架構設計 ............................................................................................... 48
3.4.1 軟體系統流程 ............................................................................................... 49
3.4.2 軟體程式設計 ............................................................................................... 50
第四章 結果與討論 ................................................................................................... 54
4.1 整體系統結果 ............................................................................................... 54
4.2 腦波分析軟體介面 ....................................................................................... 57
4.2.1 軟體功能介紹 ............................................................................................... 59
4.2.2 實驗分析 ....................................................................................................... 64
4.3 視覺誘發電位分析結果 ............................................................................... 66
4.4 睡眠腦波分期結果 ....................................................................................... 69
第五章 結論與未來展望 ........................................................................................... 74
參考文獻 ................................................................................................................................ 75

[1] K. H. Chiappa, “Evoked potentials in clinical medicine. Third edition,” Philadelphia: Lippincott, 1997.
[2] D. Regan, “Evoked potentials in psychology, sensory psychology and clinical medicine,” Chapman and Hall, 1972.
[3] A. M. Halliday, “Evoked potentials in clinical testing,” Churchill Livingstone, 1982.
[4] N. W. Perry and D. G. Childers, “The human visual evoked response: method and theory,” Spring field: Illinois, 1969.
[5] M. J. Aminoff, “Electrodiagnosis in clinical neurology,” Churchill Livingstone, 1980.
[6] D.O. Walter, “A posteriori wiener filtering of average evoked response,” Electroenceph clin Neurophysio, vol. 27, pp. 61-70, 1969.
[7] D. J. Doyle, “Some comments on the use of Wiener filtering in the estimation of evoked potentials,” Electroenceph clin Neurophysiol, vol. 38, pp. 533-4, 1975
[8] J. P. de Weerd, “A posteriori time-varying filtering of averaged evoked potentials. I. Introduction and conceptual basis,” Biol Cybern, vol. 41, pp. 211-22, 1981.
[9] J.P. Weerd and J.I. Kap, “A posteriori time-varying filtering of averaged evoked potentials. II. Mathematical and computational aspects,” Biol Cybern, vol. 41, pp. 223-34, 1981.
[10] S. Nishida, M. Nakamura, S. Suwazono, M. Honda, T. Nagamine and H. Shibasaki, “Automatic detection method of P300 waveform in the single sweep records by using a neural network,” Med Eng Phys, vol. 16(5), pp. 425-9, 1994
[11] T. D. Lagerlund, F. W. Sharbrough and N. E. Busacker, “Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition,” J Clin Neurophysiol, vol. 14(1), pp.73-82 1997.
[12] S. Casarotto, A. M. Bianchi, S. Cerutti and G. A. Chiarenza, “Principal component analysis for reduction of ocular artefacts in event-related potentials of normal and dyslexic children,” J Clin Neurophysiol, vol. 115, pp.609-19, 2004.
[13] A. Effern, K. Lehnertz, G. Fernandez, T. Grunwald, P. David and C. E. Elger, “Single trial analysis of event related potentials: non-linear de-noising with wavelets,” Clinical Neurophysiology, vol. 111, pp. 2255-2263, 2000.
[14] P. Comon, “Independent component analysis, A new concept,” Signal Processing, vol. 36(3) , pp. 287-314, 1994.
[15] A. C. Tang, J. Y. Liu and M. T. Sutherland, “Sutherland. Recovery of correlated neuronal sources from EEG: The good and bad ways of using SOBI,”
NeuroImage, vol. 28(2), pp. 507-519, 2005.
[16] P. V. Hese, W. Philips and J. D. Koninck, “Automatic detection of sleep stages using the EEG,” Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2, pp. 1944-1947, 2001.
[17] A. Flexer, G. Georg and G. Dorffner, “A reliable probabilistic sleep stager based on a single eeg signal,” Artificial Intelligence in Medicine, vol, 33(3), pp. 199-207, 2005.
[18] C. Berthomier, X. Drouot,M. Herman-Stoica, P. Berthomier, J. Prado, D. Bokar-Thire, O. Benoit, J. Mattout and M. P. d'Ortho “Automatic analysis of single-channel sleep eeg: Validation in healthy individuals,” Sleep, vol. 30(11), pp. 1587-1595, 2007.
[19] T. Penzel and R. Conradt “Computer based sleep recording and analysis,” Sleep Medicine Review, vol. 4, pp. 131-148, 2000.
[20] F. Lopes and Silva. “Computer-assisted eeg diagnosis: Pattern recognition techniques,” Electroencephalography, Basic Principles, Clinical Applications and Related Fields, vol. 53, pp. 871-897, 1987.
[21] B. Hjorth, “Time domain descriptors and their relations to a particular model for generation of eeg activity,” Computerized EEG Analysis, pp. 3-8, 1975.
[22] E. Estrada, H, Nazeran, P. Nava, K. Behbehani, J. Burk and E. Lucas, “EEG feature extraction for classification of sleep stages,” Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 1, pp. 196-199, 2004.
[23] M. Jobert, C. Timer, E. Poiseau and H. Schulz “Wavelets - a new tool in sleep biosignal analysis,” Journal of Sleep Research, vol. 3, pp. 223-232, 1994.
[24] E. Oropesa, H, L. Cycon and M. Jobert, “Sleep Stage Classification using Wavelet Transform and Neural Network,” Proceedings of the fifth joint conference on information sciences, vol. 1(2), pp. 811-814, 2000.
[25] J. Fell, J. Röschke, K. Mann and C. Schäffner, “Discrimination of sleep stages: a comparison between spectral and nonlinear eeg measures,” Electroencephalography and Clinical Neurophysiology, vol. 98, pp. 401-410, 1996.
[26] A. U. Rajendra and F. Oliver, “Non-linear analysis of eeg signals at various sleep stages,” Computer Methods and Programs in Biomedicine, vol. 80(1), pp. 37–45, 2005.
[27] J. Cafferel, G. J. Gibson, J. P. Harrison, C. J. Griffiths and M. J. Drinnan, ”Comparison of manual sleep staging with automated neural network-based analysis in clinical practice,” International Federation for Medical and Biological Engineering, vol. 44, pp. 105-110, 2006.
[28] S. Gudmundsson, T. P. Runarsson and S. Sigurdsson, “Automatic Sleep Staging using Support Vector Machines with Posterior Probability Estimates,” Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference Intelligent Agents, Web Technologies and Internet Commerce IEEE computer society, vol. 28, pp. 366-372, 2005.
[29] T. Wei-Chih, L. Shih-Wei, T. Chin-Mong, K. Cheng-Yan and L. Hsiu-Hui, “Harmonic parameters with HHT and Wavelet transformation for automatic sleep stages scoring,” Proceedings of world academy of science, engineering and technology, vol. 35, pp.176-180, 2007.
[30] L. R. Rabiner and B. H. Juang, “An introduction to hidden markov models,” IEEE ASSP Magazine, vol. 3, pp. 4-16, 1986.
[31] G. Hansen, “Evoked Potentials-Introduction to Clinical Measurements and Evaluation,” Published by DANTEC EME Documentation Department, 1984.
[32] J. V. Odom, M. Bach, C. Barber, M. Brigell, M. F. Marmor, A. P. Tormene, G. E. Holder and Vaegan, “Visual evoked potentials standard,” Documenta Ophthalmologica, vol. 108, pp. 115–123, 2004.
[33] M. Maeda, A. Takajko, K. Inoue, K. Kumamaru, S. Matsuoka, “Time-Frequency Analysis of Human Sleep EEG and Its Application to Feature Extraction about Biological Rhythm,” SICE Annual Conference, pp. 17-20, 2007.
[34] T.W. Lee, “Independent component analysis: Theory and Application,” Kluwer Academic Publishers, 1998.
[35] A. Hyvarinen and E. Oja, “Independent component analysis: algorithms and applications,” Neural Networks, vol. 14, pp. 411-430, 2000.
[36] P. Comon, “Independent component analysis: A new concept,” Signal Processing, vol. 36, pp. 287-314, 1994.
[37] R. U. Acharya, O. N. Faust, T. Chua and S. Laxminarayan, “Non-linear analysis of EEG signals at various sleep stages,” Computer Methods and Programs in Biomedicine, vol. 80, pp.37-45, 2005.
[38] M. Akay, “Nonlinear biomedical signal processing-dynamic analysis and processing,” Institute of Electrical and Electronics Engineers, 2001.
[39] S. M. Pincus, “Approximate entropy: a complexity measure for biological time series data,” Institute of Electrical and Electronics Engineers, pp. 35-36, 1991.
[40] S. M. Pincus. “Approximate entropy as a measure of system complexity, ” Institute of Electrical and Electronics Engineers, vol. 88, pp. 2297-2301, 1991.
[41] J. Bruhn, Hi. Röpcke and A. Hoeft, “Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia,” Anesthesiology, 2000.
[42] J.S. Richman and R. J. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” Am J Physiol Heart Cire Physiol, vol. 278, pp. 2039-2049, 2000.
[43] G. Jiayi, Z, Peng and Z. Xin,W. Mingshi, “Sample entropy analysis of sleep EEG under different stages,” Proceedings of IEEE/ICME International Conference on Complex Medical Engineering, pp.1499-1502, 2007.
[44] P. Grassberger, “Procaccia, estimation of kolmogorov Entropy from a chaotic signal,” Phys Rev A, vol. 28, pp. 2591-2593, 1983.
[45] 李郁萱，利用單通道腦電波進行自動睡眠分期之快速動眼期睡眠剝奪，國立交通大學生醫工程研究所碩士論文，2008。
[46] 王文彥，以單眼眼動圖進行睡眠階段判讀，國立中山大學機械與機電工程學系研究所碩士論文，2008。
[47] 唐維志，腦波的特徵擷取用於自動睡眠分期，國立台灣大學資訊工程學研究所碩士論文，2007。
[48] 舒晨，單通道腦波睡眠品質評估系統，國立陽明大學醫學工程研究所碩士論文，2008。
[49] 湯雅雯，腦波量測系統之研製與腦波信號之非線性分析，國立成功大學電機工程學系碩士論文，2005。
[50] 陳柏村，利用Approximate Entropy與Complexity的理論來建立麻醉深度預測系統，元智大學機械工程研究所碩士論文，2006。
[51] PhysioNet, Inc., http://www.physionet.org
[52] NeuroScan, Inc., http://www.neuroscan.com/landing.cfm
[53] Compumedics, Inc., http://www.compumedics.com/products.asp?p=39#
[54] 劉勝義，“臨床睡眠檢查學”，合記圖書出版社，2004。
[55] H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Systems with Applications, vol. 36, pp. 2027-2036, 2009.