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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6791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 | Shih-Ting Liu | en |
| dc.contributor.author | 劉時廷 | zh_TW |
| dc.date.accessioned | 2021-05-17T09:18:12Z | - |
| dc.date.available | 2017-08-01 | |
| dc.date.available | 2021-05-17T09:18:12Z | - |
| dc.date.copyright | 2012-08-01 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-18 | |
| dc.identifier.citation | [1] H. M. d. Boer, M. Mula, and J. W. Sander, “The global burden and stigma of epilepsy,” Epilepsy & Behavior, vol. 12, no. 4, pp. 540–546, February 2008.
[2] A. Strzelczyk, J. Reese, R. Dodel, and H. Hamer, “Cost of Epilepsy: A Systematic Review,” PharmacoEconomics, vol. 26, no. 6, pp. 463–476, June 2008. [3] World Health Organization. International Classification of Functioning Disability and Health: ICF. Geneva: WHO; 2001. [4] Health Grades Inc, “Statistics by Country for Epilepsy,” May 2003, http://www.cureresearch.com/e/epilepsy/stats-country.htm [5] H. Witte, L. D. Iasemidis, and B. Litt, “Special issue on epileptic seizure prediction,” IEEE Trans. Biomedical Engineering, vol. 50, pp. 537–539, 2003. [6] R. S. Fisher, W. V. E. Boas, W. Blume, C. Elger, P. Genton, P. Lee, and J. Engel Jr., “Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE),” Epilepsia, vol. 46, no. 4, pp. 470–472, March 2005. [7] W. Weng and K. Khorasani, “An adaptive structure neural network with application to EEG automatic seizure detection,” Neural Network, vol. 9, pp. 1223–1240, August 1996. [8] I. Guler and E.D. Ubeyli, “Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients,” Neuroscience methods, vol. 148, no. 2, pp. 113–121, April 2005. [9] I. Guler and E.D. Ubeyli, “Multiclass support vector machines for EEG signals classification,” IEEE Trans. Information Technology in Biomedicine, vol. 11, no. 2, pp. 117–126, March 2007. [10] E.D. Ubeyli and I. Guler, “Features extracted by eigenvector methods for detecting variability of EEG signals,” Pattern Recognition Letter, vol. 28, no.5, pp. 592–603, November 2007. [11] V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate entropy-based epileptic EEG detection using artificial neural networks,” IEEE Trans. Biomedical Engineering, vol. 11 no. 3, pp. 512–518, May 2007. [12] S. Ghosh-Dastidar and H. Adeli, “Principle component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection,” IEEE Trans. Biomedical Engineering, vol. 55 no. 2, pp. 512–518, February 2008. [13] A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Epileptic seizure detection in EEGs using time frequency analysis,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 5, pp. 703–710, September 2009. [14] S. Demont-Guignard, P. Benquet, U. Gerber, and F. Wendling, “Analysis of intracerebral EEG recordings of epileptic spikes insights from a neural network model,” IEEE Trans. Biomedical Engineering, vol. 56 , no. 12, pp. 2782–2794, December 2009. [15] A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Epileptic seizure detection in EEGs using time frequency analysis,” IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 5, pp. 703–710, September 2009. [16] Shen, C.P., et al., A Multiclass Classification Tool Using Cloud Computing Architecture. International Symposium on Network Enabled Health Informatics, Biomedicine and Bioinformatics (HI-BI-BI 2012), 2012. [17] Lawrence J Hirsch, and Hiba Arif, “Electroencephalography in the diagnosis of seizures and epilepsy,” UpToDate, January 2010. [18] David P Moore, Textbook of Clinical Neuropsychiatry, Hodder Arnold Publishers, Louisville, Kentucky, USA ,June 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6791 | - |
| dc.description.abstract | 癲癇是一種常見的慢性神經疾病，並且會有不定時的發作情形。顛顯發作時病人會短暫失去肢體控制並導致生命危險。目前有關癲癇之研究及診斷多數利用腦波圖(Electroencephalogram)。腦波圖可以用不同的顯示方法被呈現，其中兩種為單極點訊號 (Unipolar)和雙極點訊號 (Bipolar)。傳統腦波訊號分析大多利用單極點訊號作為基礎，但醫師在診斷顛癇時時常利用雙極點訊號來呈現腦波圖。因此我們也把雙極點訊號拿來作為辨識系統之參考數據。我們設計了一列對於雙極點訊號之訊號處理及特徵抽取方法希望能夠改善目前現有之自動化癲癇診斷系統。在訊號處理方面我們利用了小波轉換(Wavelet Transform)將主要不同腦波頻帶抽取出來。在特徵抽取上我們利用似熵 (Approximate entropy)及種總變差(Total variation)來顯示出規則與不規則之腦波現象。在特徵排序及選擇我們採用了基因演算法 (Genetic Algorithm)和費雪分數法 (Fisher Score)。最後再利用支持向量機(Support Vector Machine)來當我們的分類器。 | zh_TW |
| dc.description.abstract | Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Seizure episodes can cause temporal paralysis of the body, which can lead to severe injuries. Electroencephalogram (EEG) is a tool commonly used for analyzing brain activity and diagnosing brain disorders. EEG can be presented under different montage schemes. This study focuses on two of the montage schemes; unipolar montage and bipolar montage. Traditionally, the most commonly used montage for automated EEG analysis is unipolar. We experiment with incorporating bipolar EEG montage for creating a classification system to classify different epileptic wave forms. A series of functions were designed for bipolar EEG montage. We used wavelet transform (WT) to decompose EEG signal into its primary sub-bands. We use Approximate Entropy and Total Variation as features designed specifically for spike and seizure detection. We used Genetic Algorithm and Fisher Score to rank and selected most influential features for classifier. Finally we use multi-class Support Vector Machine as our classifier. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-17T09:18:12Z (GMT). No. of bitstreams: 1 ntu-101-R99945045-1.pdf: 923873 bytes, checksum: c1d2b951435ce8be64c2656b1c5961f7 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES viii Chapter 1 Introduction 1 Chapter 2 Background 4 2.1 Biosignal 4 2.1.1 Electroencephalogram (EEG) 4 2.1.2 Functional Magnetic Resonance Imaging (fMRI) 8 2.2 Machine Learning and Classification 9 Chapter 3 Methods 11 3.1 System Architecture 11 3.2 Data Acquisition 12 3.3 Data Preprocessing 15 3.4 Wavelet Transformation 16 3.5 Feature Extraction 18 3.5.1 Approximate Entropy (ApEn) 19 3.5.2 Total Variation 20 3.5.3 Feature Extraction Summary 21 3.6 Feature Selection 22 3.6.1 Fisher Score 22 3.6.2 Genetic Algorithm 24 3.7 Classification 27 3.7.1 Support Vector Machines 27 3.8 Post-Classification Spike Matching 30 3.9 Experiment Design 33 Chapter 4 Experimental Results 35 4.1 Seizure and Spike Detection 35 4.1.1 All Features (Unipolar and Bipolar Montage) 35 4.1.2 Most Influential Features 36 4.1.3 Feature Extraction Using Unipolar Montage Values 38 4.1.4 Increasing Spike Recognition Rate Using Post-Classification Spike Matching 40 Chapter 5 Conclusion and Future Work 43 5.1 Conclusion 43 5.2 Future Work 43 REFERENCE 44 | |
| dc.language.iso | en | |
| dc.title | 多通道腦波特徵抽取及分析之癲癇預測系統 | zh_TW |
| dc.title | Epileptic Seizure Detection System Using Multi-Channel EEG as Basis for Classification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李鴻璋,陳中平,邱銘章,林正偉 | |
| dc.subject.keyword | 小波轉換,心電圖,支持向量機, | zh_TW |
| dc.subject.keyword | Genetic Algorithm,Fisher Score,Support Vector Machines, | en |
| dc.relation.page | 46 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2012-07-18 | |
| 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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