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系統識別號 U0026-2608202015503800
論文名稱(中文) 基於單通道腦電圖自動睡眠分期架構與低頻電磁場之睡眠影響分析
論文名稱(英文) Automatic Sleep Staging Structure Based on Single-channel EEG and Sleep Impact Analysis of Low-frequency Electromagnetic Fields
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 劉怡安
研究生(英文) Yi-An Liu
學號 N26071534
學位類別 碩士
語文別 英文
論文頁數 47頁
口試委員 指導教授-張凌昇
口試委員-王明浩
口試委員-陳俊宏
口試委員-張天豪
口試委員-黃仁景
中文關鍵字 單通道腦電圖  睡眠品質分析 
英文關鍵字 single-channel EEG  sleep quality analysis 
學科別分類
中文摘要   人們處於睡眠期間將以半有序方式歷經五個睡眠階段,分別為第一階段睡眠(S1)、第二階段睡眠(S2)、慢波睡眠(SWS)、快速動眼期睡眠(REM),清醒狀態(W),以稱為睡眠週期。
睡眠週期階段的傳統識別方式一般依賴於專業人士以視覺化方式對多項生理睡眠檢測(Polysomnography,PSG) 所記錄睡眠期間的電生理訊號進行評分,評分準則由Rechtschaffen & Kales (R&K) rules [1] 給出。
由於人工識別方法需耗費較多的時間以及人力成本,以及多項生理睡眠檢測儀器價格不斐,所採用的電生理訊號類型或通道數目也較多 [2].。因此本研究旨在提出基於單通道腦波訊號自動睡眠評分演算架構,以解決人工識別所產生的問題。
最後根據所提出的架構展示實際應用-以驗證舒曼波發射器影響睡眠階段的變化進行睡眠品質的評估。
目標:
(1) 提出基於單通道腦波訊號自動睡眠評分演算架構,以降低電生理訊號使用的類型及通道數目。
(2) 該架構毋需依賴人力監測,且評分結果與專家評分具有一致性。
(3) 架構具有複雜度低的運算。
(4) 該架構具有可解釋性,即可作為從事睡眠研究者之輔助工具。
方法:
(1) 基於序列定態性將訊號做短時間拆分。
(2) 提取單通道Fpz-Cz 之腦波時域、頻域、非線性混合特徵作為特徵集候選者,並以kruskal-wallis test無母數方法選出局部最佳特徵集合。
(3) 採用線性判別分析(Linear Discriminant Analysis ,LDA) 作為睡眠階段分類模型。
(4) 以決策函數以及自適應眾數濾波器對模型預測結果進行平滑校正。
結果:
(1) 以受試者為單位進行留一法交叉驗證,平均準確度為81%。
(2) 選擇8位受試者數據做為測試集,獲得準確度為87%;卡巴係數為0.81694。
英文摘要 During sleep, humans will repeatedly go through five stages of sleeping in a semi-ordered manner, namely First Stage (S1), Second Stage (S2), Slow-Wave-Sleep (SWS), Rapid Eye Movement (REM), Awake (W), which also known as the sleep period.
Traditional recognition of sleep period generally relies on experts to visually score the electrophysiological signals which were recorded by Polysomnography (PSG) during sleep. The scoring criteria given by the Rechtschaffen & Kales (R&K) rules [1].
Since the manual identification requires more time-consuming and labor cost, as well as PSG is expensive, the number of type and channel is greater [2]. Therefore, this study aims to propose an automatic sleep scoring architecture based on the single-channel Electroencephalography (EEG) to tackle the problems arising from manual identification.

Objective:
(1) An automatic sleep scoring architecture based on the single-channel EEG is proposed to reduce the types of electrophysiological signals and the number of channels used.
(2) The architecture does not need to rely on human monitoring, and scoring result have consistency with expert (manual identification).
(3) The architecture which has low computational complexity.
(4) The architecture which interpretable and it can be used as an auxiliary tool for sleep researchers.

Approach:
(1) Split signals into short terms based on sequence stationarity.
(2) Extraction of single-channel Fpz-Cz EEG with time domain, frequency domain, nonlinear features as feature set candidates, and non-parameter method Kruskal-Wallis test will be used to select the local optimal feature set.
(3) The machine learning model – Linear discriminant analysis (LDA) is used as a sleep stage classifier.
(4) The decision function and adaptive mode filter is used to smooth and correct model prediction results.

Result:
(1) The average accuracy of subject based leave-one-out validation were 81%.
(2) The average accuracy and kappa coefficient of 8 subjects testing data can reach 87% and 0.81694, respectively.
論文目次 中文摘要 I
ABSTRACT III
ACKNOWLEDGEMENT V
CONTENTS VI
LIST OF TABLES VIII
LIST OF FIGURES IX
CHAPTER 1 INTRODUCTION 1
1-1 Background and motivation 1
1-2 Introduction of Sleep period 2
1-3 Introduction of EEG 4
1-3-1 Electrophysiological signals and EEG 4
1-3-2 EEG Channel and 10 – 20 System: 6
1-3-3 Properties of the EEG: 8
1-4 Introduction of ELF-EMF 10
CHAPTER 2 MATERIAL AND METHOD 12
2-1 Subjects and Recordings 12
2-2 Automatic Scoring System 13
2-2-1 Preprocessing 14
2-2-2 Functions calculation 18
2-2-3 Features Cross and Selection 27
2-2-4 Model Fitting and Classification 29
2-2-5 Postprocessing 32
2-3 EMF Sleep Quality Analysis 34
CHAPTER 3 EXPERIMENT RESULT AND DISCUSSION 36
CHAPTER 4 Conclusion 42
REFERENCES 43
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