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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆(Feipei Lai),邱銘章(Ming-Jang Chiu) | |
| dc.contributor.author | Chia-Ping Shen | en |
| dc.contributor.author | 沈家平 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:31:03Z | - |
| dc.date.available | 2017-01-27 | |
| dc.date.copyright | 2014-01-27 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-12-24 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58788 | - |
| 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。由於腦波圖 (Electroencephalogram)訊號在癲癇的診斷上扮演重要的角色。因此，雖然多頻道的腦波圖比單頻道的腦波圖有著更多的資訊以及空間解析度，但是傳統的腦波訊號分析卻缺乏多頻道的演算法。基於腦波多頻道的大量資料運算，因此我們在本篇論文提出了一個雲端架構的多頻道腦波之癲癇分析系統 (EAS)。在訊號分析上，我們同時考慮單極點訊號 (Unipolar)和雙極點訊號 (Bipolar)以抽取特徵，其中包含近似熵 (Approximate Entropy)以及訊號統計數值。同時，我們也採用基因演算法 (Genetic Algorithm)做特徵排序，最後再利用支持向量機 (Support Vector Machines)以及後棘波 (Spike)比對濾波器來辨識腦波訊號。實驗結果顯示，臨床資料II的棘波 (Spike)辨識率是86.69%，而發作 (Seizure)的辨識率是99.77%。同時利用臨床資料II所訓練的模型來偵測臨床資料III也可以得到91.18%的棘波 (Spike) 辨識率以及99.22%的發作 (Seizure) 辨識率。因此，我們建立了一個可靠地、及時地以及完整地 (包含醫療資訊以及訊號處理技術)棘波和發作的多頻道腦波偵測系統。 | zh_TW |
| dc.description.abstract | Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The Electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multi-channel EEG signals. Due to large data computation, we propose a cloud based Epilepsy Analysis System (EAS) on multi-channel EEG signals. Both unipolar and bipolar EEG and ECG signals are both considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features cascaded Genetic Algorithm (GA). Furthermore, EEG was also tested the performance by Support Vector Machine (SVM) and post-spike matching filters. We obtained accuracies of spikes and seizures are 86.69% and 99.77% for Clinical Data Set II. The detection system was further validated using the model trained by Clinical Data Set II on Clinical Data Set III. The system again showed high performance, with accuracies of spikes and seizures are 91.18% and 99.22%. Therefore, we built up a reliable, real-time, and complete (medical information and signal processing technology) system for detecting a large variety of seizures and spikes from multi-channel EEG data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:31:03Z (GMT). No. of bitstreams: 1 ntu-102-D96945013-1.pdf: 5821808 bytes, checksum: 1f76deb6a2e24cd6434aa62a186b3966 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES x LIST OF APPENDIXES xi Chapter 1 Introduction 1 Chapter 2 Background 6 2.1 Biosignal 6 2.1.1 Electroencephalogram (EEG): 6 2.1.2 Electrocardiogram (ECG) 9 2.1.3 Functional Magnetic Resonance Imaging (fMRI) 11 2.2 System Architecture 12 2.2.1 Web Services Approach 12 2.2.2 Hadoop 13 .2.2.2.1 Hadoop distributed file system 13 .2.2.2.2 MapReduce 15 Chapter 3 Methods 17 3.1 Data Acquisition and Preprocessing 17 3.2 Feature Extraction 21 3.2.1 Wavelet Transform 21 3.2.2 Basic Statistics Features 23 3.2.3 Statistical Features of Unipolar EEG and Bipolar EEG 25 3.3 Feature Selection 28 3.3.1 Fisher Score 28 3.3.2 Genetic Algorithm (GA) 30 .3.3.2.1 Chromosome 31 .3.3.2.2 Fitness 31 .3.3.2.3 Operator 31 3.4 Classification 34 3.4.1 Support Vector Machine 34 3.4.2 K-nearest neighbors 36 3.4.3 Artificial Neural Network 37 3.5 Performance Evaluation 39 Chapter 4 Experimental Results 40 4.1 Single Channel EEG Data 40 4.2 Multi-Channel EEG Data 43 4.3 Post-Classification Spike Matching 46 4.4 System implementation 49 4.4.1 Two Layer Cloud Computing 50 4.4.2 Interface Page EEG of Monitor 53 4.4.3 System Performance 54 Chapter 5 Discussion 56 5.1 Single Channel EEG Data 56 5.2 Multi-Channel EEG Data 60 5.3 System Optimization 64 5.3.1 One Layer MapReduce and Two Layer MapReduce 64 5.3.2 Feature Reduction Method 65 Chapter 6 Conclusion 67 REFERENCE 68 APPENDIXES 74 | |
| dc.language.iso | en | |
| dc.subject | 腦波 | zh_TW |
| dc.subject | 雲端運算 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | Electroencephalogram | en |
| dc.subject | Genetic Algorithm | en |
| dc.subject | Support Vector Machine | en |
| dc.subject | Cloud Computing | en |
| dc.title | 植基於雲端運算之多通道腦波癲癇預測系統 | zh_TW |
| dc.title | Cloud-based Epileptic Seizure Detection System Using a Multi-Channel EEG Classification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳中平,高成炎,高文忠,林正偉,周迺寬 | |
| dc.subject.keyword | 腦波,基因演算法,支持向量機,雲端運算, | zh_TW |
| dc.subject.keyword | Electroencephalogram,Genetic Algorithm,Support Vector Machine,Cloud Computing, | en |
| dc.relation.page | 77 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-12-26 | |
| 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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