請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16250完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆(Feipei Lai) | |
| dc.contributor.author | Weizhi Zhou | en |
| dc.contributor.author | 周緯志 | zh_TW |
| dc.date.accessioned | 2021-06-07T18:06:43Z | - |
| dc.date.copyright | 2012-07-27 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-23 | |
| dc.identifier.citation | 1. Fisher, R.S., et al., Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia, 2005. 46(4): p. 470-2.
2. D. Hirtz, D.J.T., K. Gwinn-Hardy, M. Mohamed, A. R. Chaudhuri and R. Zalutsky, How common are the 'common' neurologic disorders? Neurology, 2007. 68: p. 326. 3. Engel, J., Jr., A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia, 2001. 42(6): p. 796-803. 4. Pelvig, D.P., et al., Neocortical glial cell numbers in human brains. Neurobiol Aging, 2008. 29(11): p. 1754-62. 5. Pakkenberg, B. and H.J. Gundersen, Total number of neurons and glial cells in human brain nuclei estimated by the disector and the fractionator. J Microsc, 1988. 150(Pt 1): p. 1-20. 6. Society, A.C.N. Guidelines for Standard Electrode Position Nomenclature. 2006; Available from: http://www.acns.org/pdfs/ACFDD46.pdf. 7. White, T., Hadoop: The definitive guide2010: Yahoo Press. 8. Ghemawat, S., H. Gobioff, and S.-T. Leung, The Google file system. SIGOPS Oper. Syst. Rev., 2003. 37(5): p. 29-43. 9. Borthakur, D. HDFS Architecture. 2010; Available from: http://hadoop.apache.org/common/docs/r0.20.2/hdfs_design.html. 10. Vishwanath, K.V. and N. Nagappan. Characterizing cloud computing hardware reliability. 2010. ACM. 11. Shafer, J., S. Rixner, and A.L. Cox, The Hadoop distributed filesystem: Balancing portability and performance, 2010, IEEE International Symposium. p. 122-133. 12. Dean, J. and S. Ghemawat, MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 2008. 51(1): p. 107. 13. Pavlo, A., et al., A comparison of approaches to large-scale data analysis, 2009, ACM. p. 165-178. 14. Hojjat Adeli, S.G.-D., Automated EEG-based diagnosis of neurological disorders2010. 15. Holland, J.H., Adaptation in natural and artificial systems1975: University of Michigan press. 16. Cortes, C. and V. Vapnik, Support-vector networks. Machine learning, 1995. 20(3): p. 273-297. 17. Chang, C.C. and C.J. Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2011. 2(3): p. 27. 18. Hsu, C.W., C.C. Chang, and C.J. Lin, A practical guide to support vector classification, 2003. 19. Boser, B.E., I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the fifth annual workshop on Computational learning theory1992, ACM: Pittsburgh, Pennsylvania, United States. p. 144-152. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16250 | - |
| dc.description.abstract | 生醫訊號的分析與分類在醫學領域上是一項重要的課題。在機器學習中，建立模型與分析生理訊號用來找出病人的生理訊號模式。這步驟包括使用權重來指示生理訊號與病人狀態的不同關係。我們建立一個包含了抽取特徵和分類器的分析系統。使用者可用這個系統以分析生醫訊號。並給使用者檢查分類器的判斷結果正確與否給予系統回饋的功能，使得系統在使用者使用後可以越來越進步。我們使用雲端平台加速癲癇患者腦波圖分析的流程。在抽取特徵部分使得本來需要1920分鐘的時間才能跑完的資料降低到只需要85分鐘就可以跑完。在分類器部分使得本來需要2058分鐘的時間的資料降低到只需要119分鐘就可以跑完。本研究改善了抽取特徵和分類器所需的時間並能透過使用者的回饋改善準確率。 | zh_TW |
| dc.description.abstract | The analysis and classification of biomedical signals is an important issue in medical field. In machine learning, building models and analyzing physiological signals are used to find the physiological signal pattern of patient status. The process uses weighting, which indicates different relationships between physiological signals and patient status. We build up a distributed system which contains feature extraction and classification. Users can utilize this system to analyze biomedical signals and give feedbacks to the system. After users give feedback, the accuracy of classification can be improved. We utilized this cloud platform to accelerate the process of electroencephalography signal analyzing in seizure patients. In the feature extraction part, it reduces the computing time from 1920 minutes to 85 minutes. In the classifier part, it reduces the computing time from 2058 minutes to 119 minutes. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T18:06:43Z (GMT). No. of bitstreams: 1 ntu-101-R99945040-1.pdf: 1656647 bytes, checksum: 000c0c5d7eaa03bd74dc9113a16ffe59 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Background 1 1.1 Epilepsy 1 1.2 General procedure of seizure prediction 1 1.2.1 The principle of neuron discharge 2 1.3 Electroencephalography 4 1.3.1 Bipolar pattern of Electroencephalography 4 1.3.2 Spike-and-wave 6 Chapter 2 System Architecture 7 2.1 General Architecture 8 2.2 Web service 9 2.3 Hadoop 10 2.3.1 Hadoop Distributed File System 11 2.3.2 MapReduce 14 Chapter 3 Methodology 16 3.1 Execution Process 16 3.2 EEG Database 18 3.3 Feature Extraction 22 3.3.1 Wavelet Transform 22 3.3.2 Approximate Entropy (ApEn) 24 3.4 Feature Selection 25 3.4.1 Genetic Algorithm 26 3.4.2 Fisher Score 28 3.5 Support Vector Machine 28 3.6 Evaluation 30 Chapter 4 Result 31 4.1 System Implementation 31 4.1.1 User Interface 31 4.2 Supervised learning 32 4.3 Performance 33 Chapter 5 Conclusion 34 5.1 Limitations 34 5.2 Future work 35 Bibliography 36 | |
| dc.language.iso | en | |
| dc.title | 植基於雲端運算之生醫訊號辨識系統 | zh_TW |
| dc.title | Biomedical Signal Analytic System Based On Cloud Computing | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳中平(Chung-Ping Chen),李鴻璋(Hung-Chang Lee),林正偉(Jeng-Wei Lin),邱銘章(Ming-Jang Chiu) | |
| dc.subject.keyword | 雲端運算,生醫訊號,支援向量機,腦波分析, | zh_TW |
| dc.subject.keyword | Cloud computing,Biosignal,Support Vector Machine,EEG analysis, | en |
| dc.relation.page | 36 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2012-07-24 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-101-1.pdf 目前未授權公開取用 | 1.62 MB | Adobe PDF |
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