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研究生:周緯志
研究生(外文):Weizhi Zhou
論文名稱:植基於雲端運算之生醫訊號辨識系統
論文名稱(外文):Biomedical Signal Analytic System Based On Cloud Computing
指導教授:賴飛羆賴飛羆引用關係
指導教授(外文):Feipei Lai
口試委員:陳中平李鴻璋林正偉邱銘章
口試日期:2012-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生醫電子與資訊學研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:36
中文關鍵詞:雲端運算生醫訊號支援向量機腦波分析
外文關鍵詞:Cloud computingBiosignalSupport Vector MachineEEG analysis
相關次數:
  • 被引用被引用:0
  • 點閱點閱:324
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
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。

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.

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


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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.


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