||Automatic Sleep Staging Structure Based on Single-channel EEG and Sleep Impact Analysis of Low-frequency Electromagnetic Fields
||Department of Electrical Engineering
sleep quality analysis
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  給出。
(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。
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 .
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 . 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.
(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.
(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.
(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.
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
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