臺灣博碩士論文加值系統

根據世界衛生組織在2015 年的報告顯示，癲癇疾患每年影響了世界上3.3% 的人口。有高達32.5% 的患者因為各種原因而放棄的抗癲癇藥物的治療。其中有 12.4% 的患者因為藥物副作用所造成的困擾而放棄治療 ; 有 11.6% 因為所開立的抗癲癇藥物無顯著療效而選擇性地放棄治療 ; 最後，有剩下的 8.5% 的患者因為上述兩者原因而最終放棄治療。因此，如何提升患者依從性與有效率的選用藥劑成為相當重要的環節。為了能夠從腦波中擷取足夠的潛在資訊，在此研究中使用了時域上的頻率變化轉換。在這個研究中使用了八個種演算來針對腦波非線性與非穩態之特性來進行特徵提取。研究結果顯示，針對癲癇發生時之腦波進行轉換與演算後再進行支持性向量機的雙向比對所得到對於服用Phenobarbital 與Phenytoin 的患者有73.95%的辨認精準度 ; 對於服用Phenobarbital 與 Levetiracetam 的患者有64.75% 的辨認精準度 ; 對於服用Phenytoin 與 Levetiracetam的患者有68.25% 的辨認精準度。然而，完整的腦波 (其中包含了發作期間、正常期間、發作前期間) 經過運算與特徵擷取後放入支持向量機分類取得了96%, 91.25%, and 97.5% 的比對精準度。最後針對三種用藥者的腦波進行運算後的預兆期 (發作前 10ms) 與發作期進行比對後針對Phenobarbital 得到了80% ; 針對Levetiracetam 得到了75% ; 針對 Phenytoin 得到了85%的辨認精準度。最後結論，此研究證明了癲癇腦預兆期的存在並且可以使用 EEG 演算後偵測到。另外，對於針對時域-頻率變化上，訊號震盪與了解癲癇波段的腦波最有關聯性。

In accordance to the World Health Organization (WHO)’s statistical review in 2015, epilepsy has been affecting almost 3.3% of global population every year. According to previous research. There are more than 32.5% of patient has invalid control of epilepsy. Among them, there are 12.4% quit the treatment because of the adverse effect of AED, 11.6% stop accepting the medication due to the lack of efficacy of the AED prescribed, and 8.5% of patients quit because of mixture of two issues Thus providing a method for reducing the non-adherence problem during the AED therapy is crucial. In order to extract adequate encrypted information from extracranial EEG, a temporal-frequency study has been performed in data preparation. Additionally, eight features were taken into consideration to recognize the non-stationary and non-linear epileptiform of EEG. In the result, an average of 73.95% accuracy, 64.75% accuracy, and 68.25% accuracy were performed in an SVM pattern recognition for the pair-comparison of ical EEG segment between Phenobarbital versus Phenytoin, Phenobarbital versus Levetiracetam, and Phenytoin versus Levetiracetam respectively. With the pair-comparison the accuracy for classifying of the whole spectrum EEG of Phenobarbital versus Phenytoin, Phenobarbital versus Levetiracetam, and Phenytoin versus Levetiracetam intakes were 96%, 91.25%, and 97.5% respectively. For the recognition of ictal and pre-ictal segregation, the EEG of patients gotten prescribed with Phenobarbital, Levetiracetam, and Phenytoin were 80%, 75%, and 85% accuracy respectively. In the conclusion, this research proved the existence of pre-ical activity, and the signal fluctuation of temporal-frequency information is the most relatable features for recognizing onset movement of a seizure.

口試委員會審定書 LETTER OF COMMISIONER VERIFICATION i
誌謝 AKNOWLEDGEMENT ii
中文摘要CHINESE ABSTRACT iii
英文摘要ENGLISH ABSTRACT iv
1. INTRODUCTION 1
1.1. INEFFECTIVENESS OF CURRENT AED TREATMENT 1
1.2. DEFICIENCY OF CLINICAL ELECTROPHYILOGICAL RECORDINGS 2
2. BACKGROUND 3
2.1. EPILEPTIC SEIZURE 3
2.1.1. PATHOLOGICAL DEFINITION 3
2.1.2. CLASSIFICATION 3
2.2. INTRA-STIMULATION 5
2.2.1. VAGUS NERVE NEUROSTIMULATION 5
2.2.2. DEEP BRAIN STIMULATION 7
2.2.3. RESPONSIVE NEUROSTIMULATION 8
3. LITERATURE REVIEW 8
3.1. TEMPROAL INFORMATION OF EPILEPTIC SEIZURE 8
3.2. SIGNAL PROSSESSING OF EPILEPTIFORM 10
3.3. EARLY APPROACH OF ANTICIPATING ONSET 10
3.4. HILBERT-HUANG TRANSFORMATION BASED PROCESSING 11
4. METHODOLOGY 12
4.1. SUBJECTS 13
4.2. IPSILATERAL EAR REFERENCE ORIENTATION OF EEG 13
4.3. INTRINSIC MODE FUNCTION HILBERT-HUANG 15
4.4. APPLYING EEG FEAGURE SELECTION AND EXTRACTION 17
4.5. APPLYING SUPPORT VECTOR MACHINE ON EEG FEATURES 19
5. RESULT 21
5.1. SVM CLASSIFICATION ON DIFFERENT AED INTERFERED EEG 21
5.2. SVM SELECTED FEATURES 23
5.3. PRE-ICTAL PERIOD DETECTION 27
6. CONCLUSION AND DISCUSSION 28
6.1. THE EXISTENCE OF PRE-ICTAL PERIOD 28
6.2. AED EFFECT ENCRYPTION IN THE EEG 28
6.3. NUMBER OF EXTREMA AND ZERO-CROSSING REPRESENTATION 29
7. REFERENCE 30

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