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认知与科技-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

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.
-

PSG Recording

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


Sleep Lab


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


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


Actiwatch


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


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


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.

Portable PSG for Homecare


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.


Agreement Evaluation


PSG Signals

spindle K-complex

delta waves

Fast eye movements

absent EMG


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.

Stage Wake
Abundant alpha wave (8-12Hz)
Siesta
802
EEG
Our PSG

Siesta
802
ROC-LOC
Our PSG

Siesta
802
EMG
Our PSG


Stage 2
Spindles 1s K-complex 2s
Siesta
802
EEG
Our PSG

Siesta
802
ROC-LOC
Our PSG

Siesta
802
EMG
Our PSG
20

SWS
9s 2s
Siesta
802
EEG
Our PSG

Siesta
802
ROC-LOC
Our PSG

Siesta
802
EMG
Our PSG

21

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


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


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。


法则式自动睡眠判读系统
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.

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)


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)


效能评估
? 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%


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.

Sleep Scoring System


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?


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