請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38021完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
| dc.contributor.author | Robert Lin | en |
| dc.contributor.author | 林進富 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:57:02Z | - |
| dc.date.available | 2009-06-16 | |
| dc.date.copyright | 2008-06-16 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-06-09 | |
| dc.identifier.citation | 參考文獻
中谷義雄。1973a。良導絡理論的研究。初版，1-11。台北：日新文化出版社。 中谷義雄。1973b。良導絡測定法。初版，1-6。台北：日新文化出版社。 中國科學技術大學生物醫學工程跨系委員會。1993。神經網路及其應用。初版，4-19。台北：儒林圖書有限公司。 王筱涵。2006。以血氧飽和濃度檢測睡眠呼吸中止症。碩士論文。高雄：國立中山大學機械與機電工程所。 杜益昌。1999。多通道腦波機系統之設計與裝置。碩士論文。台北：台灣大學電機工程所。 邢玉瑞。2004。「黃帝內經」理論與方法論。陜西科學技術出版社。 吳進安。1998。神經診斷學。初版，59-173。台北：揚智文化事業股份有限公司。 吳炎法。2004。研製無線化多通道腦波系統並運用NAB方法進行腦波分類之研究。碩士論文。台北：台北科技大學電腦通訊與控制研究所。 奈元科技有限公司。2002。經絡能量分析儀系統操作手冊。台北：奈元科技有限公司 林昇甫、洪成安。1995。神經網路入門與圖樣辨識。二版，1-20。台北：全華科技圖書股份有限公司。 林國肇。1999。以腦波為控制信號之身心障礙鍵盤輸入系統。碩士論文。台北：台灣大學電機工程學研究所。 林能毅。2002。十六通道腦電波訊號擷取晶片之研製。碩士論文。桃園：中原大學醫學工程所。 林伯星。1999。數位腦波機系統之人機介面設計與研發。碩士論文。台北：台灣大學電機工程所。 周仁偉。1999。高、低頻率電針刺激手、足三里穴對人體胃電圖、胃腸道激素和血糖之影響。碩士論文。台中：中國醫藥學院中國醫學研究所。 周恆寬。2005。運用小波類神經於睡眠窒息腦波之辨識。碩士論文。桃園：龍華科技大學電機工程所。 禹帆。2001。無線藍芽技術的深入探討。初版。台北：松崗文魁。 袁繼銓。2003。以類神經網路預測燒傷病患住院日之研究。碩士論文。高雄：中山大學資訊管理所。 高美媚、蔡秀純、溫小娟、廖美華、羅瑋瑜、葉濬毅、張林松、張澎心、張懿欣、陳敏惠、楊佳璋、鄧貞賢、劉詩蓉、蕭明裕、謝幸媚、顧雅真譯。2002。人體生理學。三版。190-214。台北：新文京開發出版有限公司。 陳志偉。2002。研究以小波神經網路作μ波即時識別。 碩士論文。台南：成功大學機械工程所。 張清樹。2002。基於AR2迴歸隨機模型利用反射式超音波做骨質疏鬆量度的研究。碩士論文。台北：台北科技大學電通所。 張智星。2000。MATLAB程式設計與應用。初版。台北：清蔚科技股份有限公司出版事業部。 張光遠。1997。低高頻率電針刺激足三里穴對人體生理功能之影響：脈搏、血壓、皮溫、腋溫。碩士論文。台中：中國醫藥學院中國醫學研究所。 莊承澤。2001。利用數位訊號處理與智慧型理論針對正常人初期睡眠之腦波分析研究。碩士論文。桃園：元智大學機械工程研究所。 傅心家。1991。神經網路導論。初版，2-23。台北：第三波文化事業股份公司。 溫國棟。2002。智慧化臨床資訊自動篩選與分析技術研究。碩士論文。桃園：中原大學醫學工程所。 飛思科技產品研發中心。2003。MATLAB6.5輔助小波分析與應用。電子工業出版社。 蒙以正。2001。以MATLAB 透視DSP。碁峰資訊。 楊勝文。2002。可攜式智慧型腦波記錄器。碩士論文。台北：台灣大學醫學工程學研究所。 葉怡成。2003。類神經網路模式應用與實作。初版。台北：儒林圖書有限公司。 趙志峰。2005。基於小波轉換之腦電訊號分析與長期多項生理訊號自動分類系統。碩士論文。台北：台灣大學生物產業機電工程研究所。 劉勝義。2004。臨床睡眠檢查學。初版，101-163。台北：合記圖書出版社。 劉省宏。2002。醫用電子實習。初版。台北：全華科技圖書股份有限公司。 蔡政楒。2000。睡眠圖譜。初版，69-100。台北：合記圖書出版社。 潘麒全。2003。可修正的二分群集法。碩士論文。桃園：中原大學資訊管理所。 盧明智、黃敏祥。1994。OP Amp 應用+實驗模擬。初版。台北：全華科技圖書股份有限公司。 謝文哲、吳賢財、林榮三。2003。省電型16位元單晶片MSP430入門實務。初版。台中：滄海書局. 蘇木春、張孝德。1997。機器學習：類神經網路、模糊系統以及基因演算法則。初版。台北：全華科技圖書股份有限公司。 關尚勇。2001。最新癲癇病人手冊。初版。台北：藝軒圖書出版社。 關尚勇、林吉和。2002。破解腦電波。初版。台北：藝軒圖書出版社。 Agarwal, R. and J. Gotman. 1998. Automatic EEG analysis during long-term monitoring in ICU. Electroencephalography and clinical Neurophysiology. 107: 44-58. Agarwal, R. and J. Gotman. 1999. Adaptive segmentation of electroencephalographic data using a nonlinear energy operator. Circuits and Systems, 1999. ISCAS '99. (4): 199-202. Agarwal, R. and J. Gotman, 2001. Computer-assisted sleep staging. IEEE Transactions on Biomedical Engineering 48(12): 1412-1423. Akin, M., M.A. Arserim, M.K. Kiymik, and I. Turkoglu. 2001. A new approach for diagnosing epilepsy by using wavelet transform and neural networks. Engineering in Medicine and Biology Society. 2(23): 25-28. Al-Abed, M., M. Manry, J.R. Burk, E.A. Lucas, and K. Behbehani. 2007. A method to detect obstructive sleep apnea using neural network classification of time-frequency plots of the heart rate variability. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 6101-6104. Al-Ashmouny, K.M., A.A. Morsy, and S.F. Loza. 2005. Sleep apnea detection and classification using fuzzy logic: Clinical Evaluation. Proceedings of the 27th Annual Internationa Conference of the Engineering in Medicine and Biology Society. 6132-6135. Anderson, C.W., E.A. Stolz, and S. Shamsunder. 1998. Multivariate autoregressive models for classification of spontaneous electroencephalogram during mental tasks. IEEE Transactions on Biomedical Engineering. 45 (3): 277-286. BIOPAC Systems, Inc. 2004. AcqKnowledge Software Guide. CA 93117,USA. Blanco, S., S. Kochen, O.A. Rosso, and P. Salgado. 1997. Applying time-frequency analysis to seizure EEG activity. Engineering in Medicine and Biology Magazine. IEEE 16(1): 64-71. Bodenstein, G., W. Schneider, and C.V.D. Malsburg. 1985. Computerized EEG pattern classification by adaptive segmentation and probability-density-function classification. Description of the method Computers in Biology and Medicine. 15(5): 297-313. Bruce, E.N. 2001. Biomedical Signal Processing and Signal Modeling. John Wiley & Sons. Burr-Brown Company. 1996. INA128 Data Sheet. Childers, D.G. 1997. Probability and Random Processes: Using MATLAB with Applications to Continuous and Discrete Time Systems. Irwin, Chicago. Chun, S.Y., U.H. Ji, and H.T. Nagle. 2004. Observations of the current change on acupuncture spots at the meal time. Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems IEEE. 490-495. Colditz, P.B., C.J. Burke, and P. Celka. 2001. Digital processing of EEG signals. IEEE Engineering in Medicine and Biology Magazine. 20(5): 21-22. Güler, I., M.K. Kiymik, M. Akin, and A. Alkan. 2001. AR spectral analysis of EEG signals by using maximum likelihood estimation. Computers in Biology and Medicine. 31(6): 441-450. Güler, N.F., M.K. Kiymik, and I. Güler. 1995. Comparison of FFT and AR-based sonogram outputs of 20 MHz pulsed doppler data in real time. Computers in Biology and Medicine. 25(4): 91-383. Hamza, M.A., P.F. White, H.E. Ahmed, and E.A. Ghoname. 1999. Effect of the frequency of transcutaneous electrical nerve stimulation on the postoperative opioid analgesic requirement and recovery profile. Anesthesiology. 91:1232-1238. Hassanpour, H., M. Mesbah, and B. Boashash. 2003. A time-frequency approach for spike detection. ICECS, 1(10): 14-17. Hoeve, M.J., R.D. Jones, G.J. Carroll, and H. Goelz. 2001. Automated detection of epileptic seizures in the EEG. Proceedings of 23rd International Conference of IEEE Engineering in Medicine and Biology Society. 1: 943-946. Hopfield, J.J. 1982. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA. 79(8): 2554-2558. Ifeachor, E.C. and B.W. Jervis. 2002. Digital Signal Processing: A Practical Approach. 2nd ed., Prentice Hall. Intersil. 2002. HIN232: +5V Powered RS-232 Transmitters/Receivers. Jain, A.K., M.N. Murty, and P.J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys. 31(3). Jiang, M.F., S.S. Tseng, and C.M. Su. 2001. Two-phase clustering process for outliers detection. Pattern Recognition Letters. 22: 691-700. Jovanov, E., A. O’Donnel, A. Morgan, B. Priddy, and R. Hormigo. 2002. Prolonged telemetric monitoring of heart rate variability using wireless intelligent sensors and a mobile gateway. Proceedings of the Second Joint EMBS/BMES Conference. (3): 1875-1876. Kachenoura, A., H. Gauvrit, and L. Senhadji. 2003. Extraction and separation of eyes movements and the muscular tonus from a restricted number of electrodes using the independent component analysis. Engineering in Medicine and Biology Society. 3: 2359-2362. Kaiser, J.F. 1990. On a simple algorithm to calculate the energy of a signal. 1990 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP1990). 381-384. Kay, S.M. 1988. Modern spectral estimation. Prentice-Hall, Inc., Englewood Cliffs, NJ. Liu, H.S., T. Zhang, and F.S. Yang. 2002. A multistage, multimethod approach for automatic detection and classification of epileptiform EEG. IEEE Transactions on Biomedical Engineering. 49 (12): 1557-1566. Mariano, C.C., H.P. Elena, and M.B. Vicente. 2004. Intelligent diagnosis of sleep apnea syndrome. IEEE Engineering in Medicine and Biology Magazine. 23 (2): 72-81. Marple, S., and Jr. Lawrence. 1987. Digital spectral analysis with applications. Prentice-Hall, Inc., Englewood Cliffs, NJ. Martens, W.L.J. 1999. Segmentation of `rhythmic' and `noisy' components of sleep EEG, heart rate and respiratory signals based on instantaneous amplitude, frequency, bandwidth and phase. Proceedings of the First Joint BMES/EMBS Conference Serving Humanity, Advancing Technology. 2: 959. Martin, T.H., B.D. Howard, and B. Mark. 1996. Neural Network Design. Thomson Learning. Singapore. 11: 1-43. Martinez, W.L. and A.R. Martinez. 2002. Computational Statistics Handbook with MATLAB. Chapman and Hall. McNicholas, W. T. and E.A. Phillipson. 2002. Breathing disorders in sleep. London: W.B.Saunders. 44-56. Morgan, B., A.R. Jones, K.A. Mulcahy, D.B. Finlay, and B. Collet. 1995. Transcutaneous electric nerve stimulation (TENS) during distension shoulder arthrography : a controlled trial. Pain. 64: 265-267. Nakatani, Y. 1956. An aspect of the study of Ryodoraku. Clinic of Chinese Medicine. 3:54-56. Novak, D., D. Cuesta-Frau, T. Al ani, M. Aboy, R. Mico, and L. Lhotska. 2004. Speech recognition methods applied to biomedical signals processing. Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (1): 118-121. Oscar, F.R., G.B. Bertha, A.B. Amparo, and M.B. Vicente. 2005. A new method for sleep apnea classification using wavelets and feedforward neural networks. Artifical Intelligence in Medicine. 34(1): 966-971. Park, H.J., D.U. Jeong, and K.S. Park, 2002. Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method. IEEE Transactions on Biomedical Engineering. 49(12):1526-1533. Plotkin, E.l. and M.N.S. Swamy, 1992. Nonlinear signal processing based on parameter invariant moving average modeling. Proceedings of Canadian Conference on Electrical Engineering and Computer Engineering (CCECE’21): TM3.11.1-TM3.11.4. Lin, R., R.G. Lee, C.L. Tseng, Y.F. Wu, and J.A. Jiang. 2006a. Design and implementation of wireless multi-channed EEG recording system and study of EEG clustering method. Biomedical Eng. Applications, Basis, and Communications. 18(6): 276-283. Lin, R., R.G. Lee, C.L. Tseng, H.K. Zhou, C.F. Chao, and J.A. Jiang. 2006b. A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks. Biomedical Eng. Applications, Basis, and Communications. 18(3): 138-143. Samir, V.M. 1994. Wavelet Analysis as a Potential Tool For Seizure Detection, Time-Frequency and Time-Scale Analysis. Proceedings of the IEEE-SP International Symposium on: 584-587. Schaul, N. 1998. The Fundamental neural mechanisms of electroencephalography. Electroencephalography and Clinical Neurophysiology. 106: 101-107. Shimada, T., T. Shiina, and Y. Saito. 2000. Detection of characteristic waves of sleep EEG by neural network analysis. IEEE Transactions on Biomedical Engineering. 47(3): 369-379. Silage, D.A., 1990. Spectral and coherence analysis applied to sleep apnea. IEEE Engineering in Medicine and Biology Magazine. 9(1): 76-78. Sugi, T., M. Nakamura, T. Shimokawa, and F. Kawana. 2003. Automatic detection of EEG arousals by use of normalized parameters for different subjects. Biomedical Engineering. 146-147. Tarassenko, L., J. Pardey, and S. Roberts. 1996. A review of parametric modeling techniques for EEG analysis; Med. Eng. Phys, 18(1): 2-11. Texas Instruments Company. 2002. MSP430x4xx Family User's Guide. Tian, J.Y. and J.Q. Liu. 2005. Apnea detection based on time delay neural network. Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society. 2571-2574. Tian, L., D. Erdogmus, M. Pavel, and S. Mathan. 2006. Automatic Frequency Bands Segmentation Using Statistical Similarity for Power Spectrum Density Based Brain Computer Interfaces. 2006 International Joint Conference on Neural Networks. 4650-4655. Tompkins, W.J. 1993. Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC. Prentice-Hall, Inc.. Tsuei, J.J. 1998. A modern interpretation of acupuncture and the meridian system. The 2nd International Conference on Bioelectromagnetism. Melbourne, Vic., Australia. 177-182. Tulga, K. and O. Ozdamar. 1995. Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Engineering In Medicine and Biology. 14(2): 160-166. Universal Scientific Industrial. 2003. UB1-1111 USI Bluetooth Class 2 Module Data Sheet. Universal Scientific Industrial. Walsh, D.M., A.S. Lowe, K. McCormack, and J.C. Willer. 1998. Transcutaneous electric nerve stimulation: Effect on peripheral nerve conduction, mechanical pain threshold, and tactile threshold in human. Archives of Physical Medicine and Rehabilitation. 79: 1051-1058. Werbos, P.J. 1994. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. NY: John Wiley & Sons. Zhao, Y. and G. Karypis. 2002. Evaluation of Hierarchical Clustering Algorithms for Document Datasets. Technical Report of University of Minnesota. 網站資料 台灣睡眠醫學學會。2004。睡眠論壇(TSSM Sleep Forum)。桃園：台灣睡眠醫學學會(TSSM)。網址：http://www.tssm.org.tw/main.php。上網日期：2004-04-10。 ASAA. 2004. Sleep Apnea Support Forum Index American. Washing, DC. ASAA. Sleep Apnea Association. Available at: www.sleepapnea.org. 22 April 2004. PhysioBank. 2004. Physiologic signal archives for biomedical research:MIT-BIH Polysomnographic Database. Cambridge, USA. Available at: www.physionet.org. 26 May 2004. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38021 | - |
| dc.description.abstract | 本篇論文主要是利用一無線數位化腦波檢測系統，進行睡眠呼吸阻斷症研究與腦波及良導絡相關性研究。此腦波擷取系統使用藍芽(Bluetooth)晶片模組與具有省電且功能強大的MSP430微控制器(Microcontroller, MCU)，整合前置放大器、濾波器、增益放大器、及數位控制電路研製而成。本裝置能迅速地擷取腦波訊號于以數位化並將之透過藍芽模組傳至PC端。PC主機可將病患之已數位化的腦電波訊號，以NAB (Non-linear energy operator, AR model, and Bisecting k-means algorithm)方法與Bisecting k-means algorithm方法進行分類分析，並可將長時段腦波訊號資料分類與儲存。接著，應用小波轉換(Wavelet transform)擷取阻塞型睡眠呼吸中止症候群(Obstructive Sleep Apnea Syndrome, OSAS)所引發的腦波訊號特徵，再經由自行設計的局部特徵分析(Characteristic part analysis, CPA) 之類神經網路架構等進行學習訓練，並分析及判讀睡眠呼吸阻斷的發生過程。經系統統計與評估結果，本系統的辨識成效最高可達Sensitivity約69.64％，Specificity約44.44％，已具有臨床參考的價值。期能提供醫療專業人員作為輔助診斷的工具，進而能提升醫療服務效率。
除此之外，本研究亦藉由良導絡值與腦波訊號節律的量測，探討人類腦部活動分別處於Alpha 波、Beta波期間，與身體十二條經絡的良導絡值之關聯性。經實驗結果證實腦部活動處於不同的腦波期間，經絡的良導絡值會有明顯的差異存在，將可供醫療專業人員參考。 關鍵字：類神經網路、藍芽、腦電訊號、良導絡、阻塞型睡眠呼吸中止症候群、小波轉換。 | zh_TW |
| dc.description.abstract | In this dissertation, a digital wireless electroencephalograph (EEG) acquisition and recording system was adopted to analyze the obstructive sleep apnea syndrome and investigate the relation between the EEG signal rhythms and Ryodoraku. The EEG acquisition and recording system uses a Bluetooth chip module and an energy-saving MSP430 Microcontroller (MCU) with powerful functions, along with integrated pre-amplifiers, filters, gain amplifiers, and a digital control circuit. After quickly acquiring and digitizing EEG signals, this system transfers the signals to a PC via the Bluetooth module. The PC then uses the NAB (Non-linear energy operator, AR model, and Bisecting k-means algorithm) method and bisecting k-means algorithm to classify and analyze patients' EEG signals. The system first performs long-period EEG signal classification and storage, and then applies wavelet transforms to acquire EEG signal characteristics due to obstructive sleep apnea syndrome (OSAS). We trained a characteristic part analysis (CPA) artificial neural network designed by our group so that it could analyze and interpret the occurrence of OSAS, and compile and assess data. The system had a maximum sensitivity of approximately 69.64%, and a specificity of approximately 44.44%. This indicates that the system can provide clinical medical personnel with a valuable auxiliary diagnostic tool, improving medical service efficiency.
In addition, this research study the correlation analysis of EEG signal rhythms and Ryodoraku value of 12 acupuncture meridian of human body in Alpha wave and Beta wave brain activity periods, respectively. The experimental results have been confirmed that the Ryodoraku value had obvious difference in different brain wave periods which could provide reference for clinical medical personnel. Keywords: Artificial Neural Network, Bluetooth, EEG, Ryodoraku, Obstructive Sleep Apnea Syndrome, Wavelet Transform. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:57:02Z (GMT). No. of bitstreams: 1 ntu-97-D91631002-1.pdf: 7022988 bytes, checksum: dffbdd31a7c83c2524cdc46fae9d729f (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 目錄
誌謝 i 中文摘要 ii 英文摘要 .iii 目錄 v 圖目錄 ix 表目錄 xii 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3相關文獻探討 2 1.3.1硬體系統的研究發展 2 1.3.2訊號處理方法 3 1.4 論文架構 5 第二章腦波特性及其在臨床上的應用 7 2.1腦波之特性 7 2.2腦波之擷取 9 2.3睡眠期間之腦波狀態 13 2.4人之睡眠障礙 14 2.5睡眠窒息症 15 2.6睡眠窒息症種類 16 2.7睡眠期間之腦波覺醒 17 2.8睡眠窒息症之判讀方法 18 2.9腦波與經絡變化的關聯性 19 第三章 腦波檢測系統 21 3.1前端量測電路 21 3.1.1腦波訊號的量測處理電路 21 3.1.2微控制程式 28 3.2後端腦波訊號之分析 30 3.2.1 PC端軟體程式設計 31 3.2.2腦波訊號之切割方法 33 3.2.3分段後腦波訊號之特徵萃取 38 3.2.4腦波訊號的特徵分類 40 3.3腦波分析演算法應用之數值範例 42 第四章 睡眠窒息症的判讀 49 4.1睡眠窒息症的腦波訊號特徵 49 4.2睡眠資料庫說明 50 4.3小波轉換理論 53 4.4類神經網路 59 4.5實驗方法與技術 62 4.5.1每秒局部特徵分析法 63 4.5.2片段連續特徵分析法 68 4.5.3片段特徵局部分析法 72 第五章 腦波與良導絡關聯性研究 78 5.1實驗儀器 78 5.1.1良導絡量測儀器 78 5.1.2腦電波量測儀器 81 5.2實驗步驟設計 83 5.3良導絡測定法 85 5.4腦波節律的量測法 86 第六章 結果與討論 89 6.1腦波機模組化電路與訊號測試 89 6.2人體腦波之量測結果 91 6.3腦波訊號之分類結果 94 6.4睡眠窒息腦波特徵之辨識結果 100 6.5良導絡與腦波節律之量測結果 105 6.5.1實驗設計 105 6.5.2量測結果 105 6.5.3實驗結果討論 112 第七章 結論與未來展望 114 7.1結論 114 7.2未來展望 116 參考文獻 118 個人著作目錄 128 | |
| dc.language.iso | zh-TW | |
| dc.subject | 阻塞型睡眠呼吸中止症候群 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 藍芽 | zh_TW |
| dc.subject | 腦電訊號 | zh_TW |
| dc.subject | 良導絡 | zh_TW |
| dc.subject | 小波轉換。 | zh_TW |
| dc.subject | Artificial Neural Network | en |
| dc.subject | Bluetooth | en |
| dc.subject | EEG | en |
| dc.subject | Wavelet Transform. | en |
| dc.subject | Obstructive Sleep Apnea Syndrome | en |
| dc.subject | Ryodoraku | en |
| dc.title | 應用可攜式腦波機於睡眠呼吸阻斷
與良導絡相關性之研究 | zh_TW |
| dc.title | Applications of Portable EEG System to the Study of Relationship between OSAS and Ryodoraku | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳世銘(Suming Chen),鄭宗記(Tzong-Jih Cheng),李仁貴(Ren-Guey Lee),曾傳蘆(Chwan-Lu Tseng) | |
| dc.subject.keyword | 類神經網路,藍芽,腦電訊號,良導絡,阻塞型睡眠呼吸中止症候群,小波轉換。, | zh_TW |
| dc.subject.keyword | Artificial Neural Network,Bluetooth,EEG,Ryodoraku,Obstructive Sleep Apnea Syndrome,Wavelet Transform., | en |
| dc.relation.page | 129 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-06-10 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-97-1.pdf 未授權公開取用 | 6.86 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。