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認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
認知與科技 以睡眠為例
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認知與科技 以睡眠為例

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  1. 認知與科技-以睡眠為例 梁 勝 富成功大學 資訊工程系/資工所/醫資所 sfliang@mail.ncku.edu.tw Lab:神經運算與腦機介面實驗室 http://ncbci.csie.ncku.edu.tw/ Nov. 27, 2012 NCKU 神經運算與腦機介面實驗室
  2. Sleep• Approximately 1/3 of the human lifespan is spent in sleeping.• An 8 hour sleep comprises 4 or 5 sleep cycles.• Each cycle lasts approximately 90 minutes and comprises different stages including • ght sleep (Stages 1 & 2), deep sleep (Slow Wave Sleep), rapid eye movement (REM).Wake S1 REM REM REM REM REM S2SWS 1 2 3 4 5 6 7 8 NCKU 神經運算與腦機介面實驗室
  3. Sleep Problems• A considerable portion of population in the world have sleep problems, including insomnia (~30%) and sleep apnea (2-4%).• Sleep diseases seriously affect a patient’s quality of life such as causing daytime sleepiness, irritability, depression, unexpected accidence, etc.• To deal with these problems, the first step is to do effective and efficient sleep diagnosis. NCKU 神經運算與腦機介面實驗室
  4. Sleep Diagnosis • All-night polysomnographic (PSG) recordings – electroencephalograms (EEGs), – electrooculograms (EOGs), – electromyograms (EMGs), are usually acquired from patients in hospitals or sleep centers. • Problems – First night effect in in an unfamiliar environment. – Disturbance from multiple recording wires of PSG affects sleep quality. – Visual sleep scoring is a time-consuming and subjective process.- NCKU 神經運算與腦機介面實驗室
  5. PSG Recording(www.neurocode-ag.com/homepage.html) NCKU 神經運算與腦機介面實驗室
  6. Sleep Lab NCKU 神經運算與腦機介面實驗室
  7. Sleep Monitoring for Homecare• The Actiwatch and Portable PSG were developed for rough and detailed sleep monitoring. Polysomnography(PSG) Actiwatch Sleep scoring for Actiwatch Sleep scoring for PSG NCKU 神經運算與腦機介面實驗室
  8. Actiwatch• The actiwach measure the movement activities of the user during sleep and the developed scoring method can analyze the recordings and report the sleep quality of the user. http://www.wantchinatimes.com/news-subclass-cnt.aspx?cid=1204&MainCatID=12&id=20110706000001 NCKU 神經運算與腦機介面實驗室
  9. Actiwatch NCKU 神經運算與腦機介面實驗室
  10. Consistency Comparison (a) Result of sleep efficiency of Sadeh’s method (b) Result of sleep efficiency of Jean-Louis’s method (c) Result of sleep efficiency of Sazonova’s method (d) Result of sleep efficiency of Tilmanne’s method (e) Result of sleep efficiency of our method NCKU 神經運算與腦機介面實驗室
  11. Overnight Scoring Sadeh, 1994 sleep wake 100 200 300 400 500 600 700 800 900 (a)Jean-Jouis, 2001 sleep wake 100 200 300 400 500 600 700 800 900 (b)Sazonova, 2004 sleep wake 100 200 300 400 500 600 700 800 900 (c)Tilmanne, 2009 sleep wake 100 200 300 400 500 600 700 800 900 (d) Our algorithm sleep wake 100 200 300 400 500 600 700 800 900 (e) PSG sleep wake 100 200 300 400 500 600 700 800 900 (f) (I2MTC, 2011) Epoch NCKU 神經運算與腦機介面實驗室
  12. User Interface An example of bad sleep quality (sleep efficiency:46.97%) 原始訊號 動作能量 判讀結果各 睡 使用者睡 眠 年齡層眠 /參 清 醒 睡眠品質數 時 間 比 睡眠問題 NCKU 神經運算與腦機介面實驗室
  13. Portable PSG for Homecare• A modularized and distributed PSG system that is more convenient and has potential for recording at home.• It is composed of multiple tiny, low-cost and wireless-synchronized signal acquisition nodes, and each node acquires specific physiological signals including, EEG, EOG EMG, airflow, respiratory bands, and blood oxygen saturation. NCKU 神經運算與腦機介面實驗室
  14. Portable PSG for Homecare NCKU 神經運算與腦機介面實驗室
  15. Novelty• Each modualized node acquires specific physiological signals within a small body region.• Novel wireless-synchronization technology is utilized to reduce sleep disturbance.• The developed system has better comfortableness performance in terms of several objective and subjective sleep indices. NCKU 神經運算與腦機介面實驗室
  16. Agreement Evaluation NCKU 神經運算與腦機介面實驗室
  17. PSG Signalsspindle K-complex delta waves Fast eye movements absent EMG NCKU 神經運算與腦機介面實驗室
  18. R&K Sleep Staging Rechtschaffen and Kales (1968) Sleep Staging CriteriaSleep Stage Scoring Criteria >50% of the page (epoch) consists of alpha (8-13 Hz) activity or low voltage, Waking mix (2-7 Hz) frequency activity. 50% of the page (epoch) consists of related low voltage mixed (2-7 Hz) Stage 1 activity. Slow rolling eye movements lasting several seconds often seen in early stage 1. Appearance of sleep spindles and/or K complexes and <20% of the epoch may Stage 2 contain high voltage (>75 μV, <2 Hz) activity. Sleep spindles and K complexes each must last >0.5 seconds. 20%-50% of the epoch consists of high voltage (>75 μV), low frequency <2 Stage 3 Hz activity. Stage 4 >50% of the epoch consists of high voltage (>75 μV), <2 Hz delta activity. Relatively low voltage mixed (2-7 Hz) frequency EEG with episodic rapid eyeStage REM movements and absent or reduced chin EMG activity. NCKU 神經運算與腦機介面實驗室
  19. Stage Wake Abundant alpha wave (8-12Hz) Siesta 802 EEGOur PSG Siesta 802ROC-LOCOur PSG Siesta 802 EMGOur PSG NCKU 神經運算與腦機介面實驗室
  20. Stage 2 Spindles 1s K-complex 2s Siesta 802 EEGOur PSG Siesta 802ROC-LOCOur PSG Siesta 802 EMGOur PSG 20 NCKU 神經運算與腦機介面實驗室
  21. SWS 9s 2s Siesta 802 EEGOur PSG Siesta 802ROC-LOCOur PSG Siesta 802 EMGOur PSG 21 NCKU 神經運算與腦機介面實驗室
  22. REM Siesta 802 EEGOur PSG Siesta 802ROC-LOC Rapid eye movementsOur PSG Siesta 802 EMG The chin EMG activity was absent or reducedOur PSG NCKU 神經運算與腦機介面實驗室
  23. Comfort Comparison1 AROUSAL NUMBER IN THE TWO-PHASE EXPERIMENT PHASE1 PHASE2 THE THE THE THE Subjects REFERENCE PROPOSED REFERENCE PROPOSED SYSTEM SYSTEM SYSTEM SYSTEM 1 15 13 19 26 2 13 12 17 12 3 18 15 16 15 4 19 12 24 9 5 9 9 11 7 6 16 13 29 18 Average 15 12.3 19.3 14.5 SD. 3.32 1.8 5.79 6.292 NCKU 神經運算與腦機介面實驗室
  24. Obstructive Sleep Apnea NCKU 神經運算與腦機介面實驗室
  25. 自動睡眠判讀• 睡眠資料往往需要專家進行人工判讀,相當費 時且可能有前後判斷不一的情況。• 開發自動睡眠判讀系統並結合可攜式PSG 可適 用於居家睡眠評估。• 可應用生醫訊號分析技術結合專家判讀規則開 發自動睡眠判讀系統。 NCKU 神經運算與腦機介面實驗室
  26. 法則式自動睡眠判讀系統 Preprocessing Feature Extraction Classification Movement epochsInput: EEG (C3-A2), Downsampling detection EOG, EMG (256Hz) Spectral / temporal feature extraction Staging with a rule (12 Features) based decision Band-pass filtering tree (14 rules) (EEG/EOG 0.5-30Hz, EMG 5-100Hz) Contextual rule Feature smoothing Segmented into Normalization 30-s epochs Movement epochs Scoring elimination Result 26 •“A Rule-based Automatic Sleep Staging Method,” Journal of Neuroscience Methods, vol. 205, no. 1, pp. 169-176, 2012. NCKU 神經運算與腦機介面實驗室
  27. 特徵分析 (Features) No. Type Feature Source Label 1 PS Total power of 0-30 Hz EEG 0-30 E 2 PS Total power of 0-30 Hz EMG 0-30 M 3 PR 0-4 Hz/0-30 Hz EEG 0-4 E 4 PR 8-13 Hz/0-30 Hz EEG 8-13 E 5 PR 22-30 Hz/0-30 Hz EEG 22-30 E 6 PR 0-4 Hz/0-30 Hz EOG 0-4 O 7 SF Mean frequency of 0-30 Hz EEG Mean(fre.) E 8 SF Mean frequency of 0-30 Hz EMG Mean(fre.) M 9 DR Alpha ratio EEG Alpha E 10 DR Spindle ratio EEG Spindle E 11 DR SWS ratio EEG SWS E 12 EMG energy Mean amplitude EMG Amp M* PS(=Power spectrum), PR(=Power ratio), SF(=Spectral frequency), DR(=Duration ratio) NCKU 神經運算與腦機介面實驗室
  28. 決策樹(Decision Tree) E: EEG Features O: EOG M: EMG 1 Wake, S1, S2, REM SWS, S1, S2, REM Alpha E 8-13 E 2 3 Wake, S1, S2 REM, S1, S2 SWS, S2 REM, S1, S2 Alpha E 0-4 E 0-30 M 22-30 E 4 5 6 7 0-30 E S2, S1 Wake, S1 REM, S1 0-30 E S2, S1 REM, S1 0-30 E S2, S1 Spindle E 0-4 E Spindle E Spindle E SWS E SWS E SWS E 0-4 O SWS S2 8 9 10 11 (9) (10) 12 13 0-4 E Mean(fre.) E 0-4 E 0-4 E Amp M Amp M Spindle E Mean(fre.) M Spindle E Spindle ES1 S2 Wake S1 REM S1 S1 S2 REM S1 S1 S2(1) (2) (3) (4) (5) (6) (7) (8) (11) (12) (13) 28 (14) NCKU 神經運算與腦機介面實驗室
  29. 效能評估 • 資料包含17位受試者所量測的14,391 30-s epochs PSG 訊號。 • 與專家判讀結果一致性超過82% 方可接受 (Norman et al., 2000; Whitney et al., 1998). Method Wake S1 S2 SWS REM Overall Our method 88.43% 35.12% 87.01% 90.8% 90.51% 86.68%Schaltenbrand et al. 91.73% 4.67% 90.61% 86.86% 79.96% 84.75% Hae-Jeong et al. 90.79% 3.04% 87.38% 69.12% 53.76% 73.95% NCKU 神經運算與腦機介面實驗室
  30. Hypnogram (睡眠結構圖) Mov Wake REM S1 S2 SWS 23:40 01:00 03:00 05:00 07:00 hr (a) Mov Wake REM S1 S2 SWS 23:40 01:00 03:00 05:00 07:00 hr (b) Mov Wake REM S1 S2 SWS 23:40 01:00 03:00 05:00 07:00 hr (c)(a) the original manually scored hypnogram, (b) the automatic staging withoutsmoothing hypnogram, and (c) the automatic staging with smoothing hypnogram. NCKU 神經運算與腦機介面實驗室
  31. Sleep Scoring System NCKU 神經運算與腦機介面實驗室
  32. Novel Tech. and Applications• EEG, EOG, EMG EEG EOG?• IF EOG is ok for sleep scoring, what is a good design for EOG measurement?• In addition to measurement, can the system provide active feedback to users?• In addition to patients, can the system benefit normal users? NCKU 神經運算與腦機介面實驗室
  33. NCKU 神經運算與腦機介面實驗室

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