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研究生:鄭珮綾
研究生(外文):Pei-Ling Cheng
論文名稱:居家睡眠評估系統-單通道腦波跟單通道眼動信號之應用
論文名稱(外文):Homecare sleep evaluation system based on single-channel EEG and single-channel EOG
指導教授:高材高材引用關係
指導教授(外文):Tsair Kao
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:醫學工程研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:60
中文關鍵詞:居家裝置睡眠評估腦波圖眼動圖
外文關鍵詞:Homecare deviceSleep evaluationEEGEOG
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睡眠品質與生活品質息息相關,在近年來有越來越多的人有睡眠方面的困擾,但是睡眠檢查的設備及資源仍是一樣地缺乏。雖然以有很多研究探討自動睡眠分期的系統及方法,試圖彌補在睡眠檢測上人力的不足,但是,這些已發展的系統大多還是建立在醫院的多通道睡眠檢測儀上,並無法確實的將睡眠檢測簡化。
在本研究中使用一套新的居家睡眠監測儀 (VitalBelt),此儀器只記錄三個生理信號,分別為腦波,眼動信號,心電圖,是個簡易,簡便適合用來做居家自我睡眠監控。從儀器記錄到的信號分別萃取出各項睡眠參數,並通過一個由”確定的判讀”,”進一步判讀”與”不合理的重讀”建立的自動睡眠分期方法做分期。實驗同時記錄醫院的多通道睡眠記錄儀跟居家睡眠監測儀,並比較醫院技術員與自動分期的結果。臨床記錄了30筆信號,整體準確率達73.48 %,而Cohen’s kappa值也有0.62,結果顯示建立在VitalBelt居家睡眠記錄儀上的自動睡眠分期系統是可信的,未來可繼續發展作為居家自我睡眠監測上。
此外,本研究另外探討在簡化的睡眠記錄及分期系統上,眼動信號是否有其存在的必要性。使用成對t檢定來比較僅使用單通道腦波的分期結果,與結合眼動信號的分期結果,結果顯示在合併眼動信號後有顯著的改善分期準確率(p<0.005)。
The quality of sleep is effective to our daily life, and more and more people have been experiencing sleep disorders in recent years. However the equipment and resources for sleep diagnosis are still limited. Although many researchers have developed automatic sleep scoring system, most of the systems are built based on PSG recording.
A new homecare sleep recording device, “VitalBelt”, which records only three channel bio-signals (EEG, EOG, ECG), is applied in our scoring system. Sleep features are extracted individually, then a scoring method including exact scoring, refinement and impossible rejection three aspect to score sleep stages. Thirty subjects were recorded using both polysnomnography and VitalBelt. Comparing the manual score by polysnomography and automatic score by VitalBelt, overall agreement was 73.48% (Cohen’s kappa = 0.62). The result showed that the automatic sleep staging system developed for VitalBelt was reliable and might apply in homecare sleep monitoring.
In addition, the methods of single-channel EEG with or without EOG were compared to evaluate the requirements of EOG for sleep staging. Paired t-test was used to estimate the agreement increase, with the result showing that the improvement was significant (P < 0.005) after combining EOG.
Abstract--------------------------------------------------------------------------------------------Ⅰ
摘要---------------------------------------------------------------------------------------------Ⅱ
CHAPTER Ⅰ INTRODUCTION--------------------------------------------------------1
1.1 Background------------------------------------------------------------------------------1
1.2 Motivation of the Thesis---------------------------------------------------------------2
1.3 Thesis Structure-------------------------------------------------------------------------2
CHAPTER Ⅱ LITERATURE REVIEW-----------------------------------------------4
2.1 Sleep Structure and Scoring Rule----------------------------------------------------4
2.1.1 Sleep Cycle---------------------------------------------------------------------4
2.1.2 R & K Sleep Staging Rule----------------------------------------------------4
2.1.3 REM Sleep---------------------------------------------------------------------6
2.2 Acquisition of Sleep Signal-----------------------------------------------------------8
2.2.1 EEG Signal Acquisition-------------------------------------------------------8
2.2.2 EOG Signal Acquisition-----------------------------------------------------10
2.3 Detection of Sleep feature------------------------------------------------------------11
2.3.1 Alpha Wave-------------------------------------------------------------------12
2.3.2 Spindle-------------------------------------------------------------------------13
2.3.3 Slow wave---------------------------------------------------------------------14
2.3.4 Detection of Rapid Eye Movement----------------------------------------14
2.4 Automatic Sleep Staging-------------------------------------------------------------16
CHAPTER Ⅲ MATERIAL AND METHODS ---------------------------------------17
3.1 VitalBelt - Homecare Sleep Staging Device---------------------------------------18
3.2 Acquisition of EEG and EOG Data-------------------------------------------------18
3.3 Sleep Data Processing----------------------------------------------------------------19
3.3.1 Adaptive Filter Design------------------------------------------------------20
3.3.2 Alpha Wave Detection-------------------------------------------------------21
3.3.3 Spindle Detection------------------------------------------------------------22
3.3.4 Slow Wave Detection--------------------------------------------------------23
3.3.5 Rapid Eye Movement Detection-------------------------------------------24
3.3.6 High-frequency EEG--------------------------------------------------------26
3.4 Automatic Sleep Staging Procedure------------------------------------------------27
3.4.1 Exact Scoring-----------------------------------------------------------------27
3.4.2 Refinement--------------------------------------------------------------------29
3.4.3 Impossible Rejection--------------------------------------------------------30
3.5 Database Used-------------------------------------------------------------------------31
3.6 Clinical Data Verified-----------------------------------------------------------------31
3.7 Sleep Quality Index-------------------------------------------------------------------32
CHAPTER Ⅳ RESULTS-----------------------------------------------------------------35
4.1 Variety of RMS Condition in Rapid Eye Movements Detection---------------35
4.2 Database Results----------------------------------------------------------------------36
4.3 Clinical Results------------------------------------------------------------------------40
4.4 Sleep Quality Evaluation-------------------------------------------------------------46
CHAPTER Ⅴ DISCUSSION-------------------------------------------------------------48
5.1 Application of RMS Condition on Rapid Eye Movements Detection---------48
5.2 Comparison of the Scoring Results of Different Methods-----------------------48
5.3 Limitations of Automatic Sleep Scoring-------------------------------------------50
5.3.1 Automatic Sleep Scoring Based on R & K Rule------------------------50
5.3.2 Limitation of Spindle Extraction by Frontal EEG----------------------52
5.3.3 Limitation of stage R Scoring by Single-channel EEG and Single-channel EOG---------------------------------------------------------52
5.3.4 Limitation of stage 1 scoring-----------------------------------------------53
5.4 Application to Sleep Quality--------------------------------------------------------55
CHAPTER Ⅵ CONCLUSIONS---------------------------------------------------------56
CHAPTER Ⅵ REFERENCES-----------------------------------------------------------57
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