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系統識別號 U0026-0812200915184504
論文名稱(中文) 睡眠分期及偵測腦波循環交替模式之自動化演算法及其應用
論文名稱(英文) An Automatic Sleep Stage Classification and Cyclic Alternating Pattern Detection Algorithm and Its Application
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 林鴻藝
研究生(英文) Hung-Yi Lin
學號 N2696418
學位類別 碩士
語文別 英文
論文頁數 85頁
口試委員 指導教授-王振興
口試委員-徐崇堯
口試委員-詹寶珠
中文關鍵字 腦波循環交替模式  腦波  睡眠分期 
英文關鍵字 Sleep Stage  CAP  EEG 
學科別分類
中文摘要 本論文提出了一個自動化睡眠分期演算法和一個自動化偵測腦波循環交替模式演算法來分析睡眠腦波,且更進一步地將兩者應用於睡眠呼吸中止症的議題上。在自動化睡眠分期的演算法中,使用了最小描述長度原理來建構一個類神經網路作為分類器,來分辨腦波訊號中的四類睡眠時期。在自動化偵測腦波循環交替模式的演算法中,使用適應性門檻值設定方法讓演算法能適應所有病人的狀況,來判斷病人在NREM睡眠中的睡眠品質是否良好。最後,上述兩個演算法將被整合且應用在臨床睡眠醫療的問題上。經由實驗結果可得知,本論文所提出的演算法已經成功地驗證了下列三點: 1) 此自動化睡眠分期演算法可以有效地將四類睡眠時間分辨出來; 2) 此自動化偵測腦波循環交替模式演算法可降低計算複雜度且達到滿意的效能; 3) 經由結合上述兩個演算法而產生的新型的睡眠呼吸中止症醫療方法,比起舊有的傳統醫療方法,能達到更好的醫療效果。
英文摘要 This thesis presents an automatic sleep stage classification and cyclic alternating pattern detection algorithm for sleep quality analysis. The proposed algorithm is applied to improve the performance of clinical therapy in obstructive sleep apnea issue. An automatic classification algorithm composed of a neural-network-based classifier, which constructed by the minimum description length (MDL) principle, is developed to classify the four types of sleep stages of electroencephalogram (EEG) signal. Subsequently, an automatic CAP detection algorithm with adaptive thresholds is utilized to evaluate the current sleep quality of the patient in the NREM stage. Finally, the abovementioned two algorithms are both applied to clinical therapy in sleep issues. The experimental results have successfully validated: 1) the proposed sleep stage classification algorithm can classify the four types of sleep stages of EEG signal efficiently; 2) the proposed CAP detection algorithm can reduce computational burden and achieve satisfactory performance; and 3) the novel therapy procedure combined with the above two algorithms can improve the performance and effectiveness of the conventional clinical therapy method in obstructive sleep apnea issue.
論文目次 CHINESE ABSTRACT........i
ABSTRACT........ii
ACKNOWLEDGEMENT........iii
CONTENTS........iv
TABLES........vii
FIGURES........viii
1 Introduction........1-1
1.1 Motivation........1-1
1.2 Literature Survey........1-2
1.3 Purpose of the Study........1-6
1.4 Organization of the Thesis........1-6
2 Automated Classification Algorithm Sleep Stage........2-1
2.1 Introduction........2-1
2.2 Sleep Structure........2-4
2.2.1 Stage W (Awake, Wakefulness)........2-4
2.2.2 Stage I (NREM 1)........2-5
2.2.3 Stage II (NREM 2)........2-7
2.2.4 Stage III (NREM 3)........2-8
2.2.5 Stage R (REM)........2-9
2.3 Automatic Sleep Stage Classification Method........2-10
2.3.1 Signal Pre-Processing........2-12
2.3.2 Feature Extraction........2-13
2.3.3 Classifier........2-14
2.3.3.1 Minimum Description Length Principle........2-15
2.4 Simulation Results........2-18
2.5 Conclusions........2-20
3 Automated Detection Algorithm for Cyclic Alternating Pattern........3-1
3.1 Introduction........3-1
3.2 Cyclic Alternating Pattern (CAP)........3-2
3.2.1 Delta Bursts........3-4
3.2.2 Polyphasic Bursts........3-5
3.2.3 K-alpha........3-6
3.2.4 Intermittent Alpha........3-6
3.2.5 EEG Arousals........3-7
3.3 Automatic CAP Detection Algorithm........3-7
3.3.1 CAP Detection Algorithm........3-8
3.3.2 Online CAP Detection Algorithm........3-13
3.4 Inter-rater Reliability........3-15
3.5 Conclusions........3-24
4 Using the Proposed Algorithm in Automatic Continuous Positive Airway Pressure........4-1
4.1 Sleep Quality........4-1
4.2 Obstructive Sleep Apnea Syndrome........4-2
4.3 Conventional Therapy Method........4-3
4.4 Improved Titration Procedure........4-5
4.5 Simulation Results........4-7
4.6 Conclusions........4-10
5 Conclusions and Future Work........5-1
5.1 Conclusions........5-1
5.2 Future Work........5-2
References........6-1
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