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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
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
 S2
SWS


          1      2      3         4   5      6      7      8
                                            NCKU 神經運算與腦機介面實驗室
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
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
PSG Recording




(www.neurocode-ag.com/homepage.html)




                                       NCKU 神經運算與腦機介面實驗室
Sleep Lab




            NCKU 神經運算與腦機介面實驗室
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
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
Actiwatch




            NCKU 神經運算與腦機介面實驗室
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
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
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
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
Portable PSG for Homecare




                 NCKU 神經運算與腦機介面實驗室
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
Agreement Evaluation




              NCKU 神經運算與腦機介面實驗室
PSG Signals


spindle   K-complex



                delta waves


                              Fast eye movements



                      absent EMG




                              NCKU 神經運算與腦機介面實驗室
R&K Sleep Staging
                    Rechtschaffen and Kales (1968) Sleep Staging Criteria

Sleep 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 eye
Stage REM
              movements and absent or reduced chin EMG activity.
                                                                  NCKU 神經運算與腦機介面實驗室
Stage Wake
             Abundant alpha wave (8-12Hz)
 Siesta
  802
  EEG
Our PSG


 Siesta
  802
ROC-LOC
Our PSG


 Siesta
  802
  EMG
Our PSG



                         NCKU 神經運算與腦機介面實驗室
Stage 2
          Spindles 1s      K-complex 2s
 Siesta
  802
  EEG
Our PSG


 Siesta
  802
ROC-LOC
Our PSG


 Siesta
  802
  EMG
Our PSG
                                          20
                                  NCKU 神經運算與腦機介面實驗室
SWS
          9s                     2s
 Siesta
  802
  EEG
Our PSG


 Siesta
  802
ROC-LOC
Our PSG


 Siesta
  802
  EMG
Our PSG

                          21
                     NCKU 神經運算與腦機介面實驗室
REM
 Siesta
  802
  EEG
Our PSG


 Siesta
  802
ROC-LOC                      Rapid eye movements
Our PSG


 Siesta
  802
  EMG     The chin EMG activity was absent or reduced
Our PSG


                                   NCKU 神經運算與腦機介面實驗室
Comfort Comparison
1                                              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              6.29

2                                                             NCKU 神經運算與腦機介面實驗室
Obstructive Sleep Apnea




                NCKU 神經運算與腦機介面實驗室
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
法則式自動睡眠判讀系統
                         Preprocessing             Feature Extraction           Classification

                                                                               Movement epochs
Input: 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
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
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 E



S1                   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
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
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 without
smoothing hypnogram, and (c) the automatic staging with smoothing hypnogram.
                                                      NCKU 神經運算與腦機介面實驗室
Sleep Scoring System




               NCKU 神經運算與腦機介面實驗室
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
NCKU 神經運算與腦機介面實驗室

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認知與科技 以睡眠為例

  • 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 S2 SWS 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 Signals spindle 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 Criteria Sleep 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 eye Stage 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 EEG Our PSG Siesta 802 ROC-LOC Our PSG Siesta 802 EMG Our PSG NCKU 神經運算與腦機介面實驗室
  • 20. Stage 2 Spindles 1s K-complex 2s Siesta 802 EEG Our PSG Siesta 802 ROC-LOC Our PSG Siesta 802 EMG Our PSG 20 NCKU 神經運算與腦機介面實驗室
  • 21. SWS 9s 2s Siesta 802 EEG Our PSG Siesta 802 ROC-LOC Our PSG Siesta 802 EMG Our PSG 21 NCKU 神經運算與腦機介面實驗室
  • 22. REM Siesta 802 EEG Our PSG Siesta 802 ROC-LOC Rapid eye movements Our PSG Siesta 802 EMG The chin EMG activity was absent or reduced Our PSG NCKU 神經運算與腦機介面實驗室
  • 23. Comfort Comparison 1 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 6.29 2 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 epochs Input: 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 E S1 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 without smoothing 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