認知與科技以睡眠為例

NCKU 神經運算與腦機介面實驗室

梁勝富

成功大學資訊工程系/資工所/醫資所

[email protected]:神經運算與腦機介面實驗室

http://ncbci.csie.ncku.edu.tw/Nov. 27, 2012

認知與科技-以睡眠為例

mailto:[email protected]

http://ncbci.csie.ncku.edu.tw/


Sleep

Wake

S2

SWS

S1REM REM REM REM REM

1 2 3 4 5 6 7 8

• 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).


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.


Sleep Diagnosis

-4-

• 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 PSGaffects sleep quality.

– Visual sleep scoring is a time-consuming and subjectiveprocess.


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.

Actiwatch

Sleep scoring for Actiwatch

Polysomnography(PSG)

Sleep scoring for PSG


Actiwatch

http://www.wantchinatimes.com/news-subclass-cnt.aspx?cid=1204&MainCatID=12&id=20110706000001

• 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.


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

(I2MTC, 2011)

100 200 300 400 500 600 700 800 900

wakesleep

(a)

100 200 300 400 500 600 700 800 900

wakesleep

(b)

100 200 300 400 500 600 700 800 900

wakesleep

(c)

100 200 300 400 500 600 700 800 900

wakesleep

(d)

100 200 300 400 500 600 700 800 900

wakesleep

(e)

100 200 300 400 500 600 700 800 900

wakesleep

(f)

Epoch

Jean-Jouis, 2001

Sazonova, 2004

PSG

Tilmanne, 2009

Our algorithm

Sadeh, 1994


User Interface

An example of bad sleep quality (sleep efficiency:46.97%)

原始訊號

動作能量

判讀結果

使用者年齡層

睡眠品質

睡眠問題

各睡眠參數

睡眠／清醒時間比


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

delta waves

K-complex

Fast eye movements

absent EMG


R&K Sleep Staging

Rechtschaffen and Kales (1968) Sleep Staging Criteria

Sleep Stage Scoring Criteria

Waking >50% of the page (epoch) consists of alpha (8-13 Hz) activity or low voltage,

mix (2-7 Hz) frequency activity.

Stage 1

50% of the page (epoch) consists of related low voltage mixed (2-7 Hz)

activity. Slow rolling eye movements lasting several seconds often seen in

early stage 1.

Stage 2

Appearance of sleep spindles and/or K complexes and <20% of the epoch may

contain high voltage (>75 μV, <2 Hz) activity. Sleep spindles and K

complexes each must last >0.5 seconds.

Stage 3 20%-50% of the epoch consists of high voltage (>75 μV), low frequency <2

Hz activity.

Stage 4 >50% of the epoch consists of high voltage (>75 μV), <2 Hz delta activity.

Stage REM Relatively low voltage mixed (2-7 Hz) frequency EEG with episodic rapid eye

movements and absent or reduced chin EMG activity.


Stage WakeAbundant alpha wave (8-12Hz)

Siesta 802

Our PSG

Siesta 802

Our PSG

Siesta 802

Our PSG

EEG

ROC-LOC

EMG


Stage 2

20

Spindles 1s K-complex 2s

Siesta 802

Our PSG

Siesta 802

Our PSG

Siesta 802

Our PSG

EEG

ROC-LOC

EMG


SWS

21

9s 2s

Siesta 802

Our PSG

Siesta 802

Our PSG

Siesta 802

Our PSG

EEG

ROC-LOC

EMG


REM

Rapid eye movements

The chin EMG activity was absent or reduced

Siesta 802

Our PSG

Siesta 802

Our PSG

Siesta 802

Our PSG

EEG

ROC-LOC

EMG


Comfort Comparison AROUSAL NUMBER IN THE TWO-PHASE EXPERIMENT 1

Subjects

PHASE1 PHASE2

THE

REFERENCE

SYSTEM

THE

PROPOSED

SYSTEM

THE

REFERENCE

SYSTEM

THE

PROPOSED

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


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。


法則式自動睡眠判讀系統

Input: EEG (C3-A2),

EOG, EMG

Preprocessing

Downsampling

(256Hz)

Feature Extraction Classification

Band-pass filtering

(EEG/EOG 0.5-30Hz,

EMG 5-100Hz)

Spectral / temporal

feature extraction

(12 Features)Staging with a rule

based decision

tree (14 rules)

Contextual rule

smoothing

Scoring

Result

Feature

Normalization

Movement epochs

elimination

Segmented into

30-s epochs

Movement epochs

detection

•“A Rule-based Automatic Sleep Staging Method,” Journal of Neuroscience Methods, vol. 205, no. 1, pp. 169-176, 2012.

26


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)

Features

Alpha E

8-13 E

Alpha E

0-30 M

0-4 E

22-30 E

0-4 E

0-30 E

Spindle E

SWS E

0-30 E

Spindle E

SWS E

0-4 E

Spindle E

Mean(fre.) E

Mean(fre.) M

0-4 E

Spindle EAmp M

0-30 E

Spindle E

SWS E

0-4 O

Amp M0-4 E

Spindle E

Wake, S1, S2, REM SWS, S1, S2, REM

Wake, S1, S2 REM, S1, S2 REM, S1, S2SWS, S2

S2, S1 Wake, S1 REM, S1 S2, S1 REM, S1 S2, S1

Wake S1S1 S2 S1 S2REM S1 S1 S2REM S1

SWS S2

E: EEG

O: EOG

M: EMG

1

2

4 5

3

6 7

8 9 10 11 12 13

(1) (2) (3) (4) (5) (6) (7) (8) (11) (12) (13) (14)

(9) (10)

28


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%


Hypnogram (睡眠結構圖)

(a) the original manually scored hypnogram, (b) the automatic staging without smoothing hypnogram, and (c) the automatic staging with smoothing hypnogram.

(a)23:40 01:00 03:00 05:00 07:00

SWS

S2

S1

REM

Wake

Mov

(b)23:40 01:00 03:00 05:00 07:00

SWS

S2

S1

REM

Wake

Mov

(c)23:40 01:00 03:00 05:00 07:00

SWS

S2

S1

REM

Wake

Mov

hr

hr

hr


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?


Documents

認知與科技 以睡眠為例

認知與科技以睡眠為例