睡眠品質是近年來備受關注的健康指標，而其中睡眠階段的分類對於評估睡眠品質及檢測睡眠狀況是重要的依據。睡眠多項生理檢查是醫院睡眠階段分類的標準，這項檢測需要技術人員的人工標記及評估、受試者需親自至醫院受測並在睡眠時戴上許多感測器，因此本研究希望使用行動裝置結合市售的單通道 EEG 穿戴式裝置、不須使用到醫療等級，就能達到簡易的自動睡眠階段分類及給予聲音刺激回饋來幫助睡眠。這篇論文睡眠階段分類預測演算法是基於散射變換來提取時移不變及變形穩定的特徵，並且使用支持向量機作為預測睡眠階段的分類器。在行動裝置上使用訓練完成的模型來幫助預測來自穿戴式裝置新數據的睡眠階段、當系統判斷受試者進入深度睡眠時會播放粉紅噪音來幫助睡眠。

Sleep quality is a popular topic in recent years. For scoring and enhancing sleep quality, sleep stage classification plays a critical role. There are a lot of novel application based on sleep stage classification. Polysomnography(PSG) is the gold standard for sleep stage interpretation that needing a technician scoring and both complex, time-consuming and uncomfortable. We would like to present the automatic sleep stage classification system that uses machine learning algorithm to implement on a mobile application that connecting a wearable single-channel EEG device. The sleep stage predicted algorithm is based on scattering transform to extract the time-shift invariant and deformation stable features. Support vector machine(SVM) is the classifier to predicted sleep stages. The trained SVM model is saved to help new data prediction on the smartphones. As the system detected the subject fall into deep sleep, the pink noise stimulation is played to enhance the slow-waves that relative to memory consolidation.

致謝
摘要
Abstract
Contents
List of Tables
List of Figures
1 Introduction 1
1.1 Sleep Stages and Sleep Quality . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Wearable EEG Headset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Medical Detection of Sleep Stages and Auditory Stimulation 11
2.1 Introduction of EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Placement of Electrodes . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.2 Waveform of EEG . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Sleep Stage Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 Category of Brain Activity and Characteristic of Sleep Stage . . . 16
2.2.2 The Whole-Night Sleep Structure . . . . . . . . . . . . . . . . . . 19
2.3 Auditory Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Proposed Sleep Stage Classification and Auditory Stimulation System 23
3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Sleep stage classification algorithm . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.3 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4 Auditory Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 Experimental Result 43
4.1 Experimental Environments, Dataset and Study Design . . . . . . . . . . 43
4.2 Smartphone APP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 RAM Usage of the Smartphone APP . . . . . . . . . . . . . . . . . . . . 47
4.4 Analysis of Sleep Stages Classification Result . . . . . . . . . . . . . . . . 48
4.4.1 Reference Sleep Stages classification Algorithm Performance . . . 49
4.4.2 Proposed Sleep Stages classification Algorithm Performance . . . 50
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5 Conclusion and Future Work 61
5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

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