An EOG-based Sleep Monitoring System and Its Application on On-line

Sleep-stage Sensitive Light Control

Chih-En Kuo

, Sheng-Fu Liang

, Yi-Chieh Li

, Fu-Yin Cherng

, Wen-Chieh Lin

, Peng-Yu Chen

Yen-Chen Liu

and Fu-Zen Shaw

The Institute of Cognitive Science, National Cheng Kung University, Tainan, Taiwan

Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan

Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

Keywords:

Sleep, Sleep Stage, Adaptive System, Electrooculogram (EOG), Interaction Design, Sleep Quality.

Abstract:

Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects

sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. The

development of an automatic sleep-staging system that does not rely upon mounting a bulky PSG or EEG

recorder on the head will enable physiological computing systems (PhyCS) to progress toward easy sleep and

comfortable monitoring. In this paper, an electrooculogram (EOG)-based sleep scoring system is proposed.

Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by

the user individually. The proposed method was found to be more than 83% accurate when compared with

the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system

encompasses adaptive brightness control of light according to online monitoring of the users sleep stages.

The experiments show that the EOG-based sleep scoring system is a practicable solution for home-use sleep

monitoring due to the advantages of comfortable recording and accurate sleep staging.

1 INTRODUCTION

In recent years, physiologically sensing technologies

have been applied to human computer interaction.

They can not only help people with disabilities but

also be integrated into general user interfaces used by

healthy people. They also create more diverse inter-

active ways and help users keep healthy (Silva et al.,

2011). Electrooculography (EOG), which measures

our eye movement, is a kind of physiological sensing

technologies. Several studies in the human computer

interaction (HCI) ﬁeld have shown that EOG can

be used to track eye gazes (Manabe and Fukumoto,

2006; Bulling et al., 2009). In addition to detecting

eye gazes, a recent study also suggested that EOG can

be used to classify people’s sleep stage(Virkkala et al.,

2007).

Sleep is important for human health. Sleep dis-

eases, such as insomnia and obstructive sleep ap-

nea, seriously affect quality of life. Sleep is not

a static stage but a dynamic process (Rechtschaf-

fen and Kales, 1968). Sleep can be divided into

six periods: wakefulness (Wake); the four stages

of non-rapid eye movement (NREM, numbered 1-

4); and rapid eye movement (REM). Stages 3 and 4

have also been combined, and referred as the slow

wave sleep stage (SWS). Conventionally, to evaluate

a subjects sleep quality, all-night PSG tests includ-

ing electroencephalograms (EEG), EOG, and elec-

tromyograms (EMG) are usually recorded and scored

by a well-trained expert (Rechtschaffen and Kales,

1968). Due to their high cost and bulk, conventional

PSG systems are not suitable for sleep recording at

home. Some easy-to-use alternative products such as

Fitbit, Bodymedia Fit, and Zeo, along with the corre-

sponding analysis software, have been developed for

home sleep testing.

The HCI ﬁeld has begun to take note of sleep-

related issues (Aliakseyeu et al., 2011; Choe et al.,

2011), and additional interaction designs to aid sleep

have been proposed. Several studies identify factors

that would affect sleep quality, and provide sugges-

tions to improve it (Stepanski and Wyatt, 2003). In

addition to self-management, advances in interaction

designs may assist users to achieve better sleep qual-

ity and habits (Aliakseyeu et al., 2011). One prior

Kuo C., Liang S., Li Y., Cherng F., Lin W., Chen P., Liu Y. and Shaw F..

An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control.

DOI: 10.5220/0004725600200030

In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014), pages 20-30

ISBN: 978-989-758-006-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

study (Bauer et al., 2012) applied the concept of pe-

ripheral display to the design of mobile applications

that can encourage users to keep good sleeping habits.

There are also various systems using sensors on mo-

bile phones to help users record sleep stages (Law-

son et al., 2013) and to understand their sleep quality.

Some products focus on waking users up by adjusting

light levels during the period near the preset wake-up

time, e.g., the Philips Wake-up Light.

Choe et al. (2011) have indicated many factors af-

fecting sleep quality, including caffeine, the bedroom

environment, and fears. Aliakseyeu et al. (2011),

meanwhile, have suggested several design opportuni-

ties for improving sleep, some of which would need

the support of real-time sleep-stage monitoring. In-

deed, the results of these studies inspired us to de-

velop an automatic scoring system. Recently, several

proposals have been made for phone-based applica-

tions (apps) and wearable devices to monitor sleep ef-

ﬁciency (wake-sleep states), using accelerometers to

detect body movements during sleep. These devices

are easy to use, but cannot accurately recognize sleep

stages and they may not function at all if used other

than in bed, e.g. while having a nap in the ofﬁce. The

development of an online sleep-staging system that

does not require the mounting of a bulky PSG sys-

tem on the head will allow PhyCS to progress toward

easier sleep and more comfortable monitoring.

This paper proposes an EOG-based sleep moni-

toring system including EOG acquisition and a sleep-

staging method based on EOG signal analysis. Com-

pared to all-night PSG or EEG recordings, EOG has

the advantage of easy placement, and can be mea-

sured by the individual user without assistance. An

automatic EOG sleep-scoring method integrating the

time-domain EOG feature analysis and a linear clas-

siﬁer is proposed. The agreement between the pro-

posed method and the expert scoring is higher than

83%, placing it within the range of inter-score agree-

ment (Norman et al., 2000). Active control of envi-

ronmental light/brightness, based on online monitor-

ing of the users sleep stages by the proposed system,

is also demonstrated.

2 BACKGROUND AND RELATED

WORK

2.1 Stages of Sleep

Depending on whether EEG, EOG, or EMG has been

utilized, sleep states can be roughly separated into

NREM and REM sleeps, which alternate throughout

a night in a roughly 90-minute cycle. In wakeful-

ness with the eyes closed, alpha rhythms (8-13 Hz)

can be observed using EEG in more than 50% of each

epoch (i.e. each 30 seconds of data). According to

the American Academy of Sleep Medicine (AASM)

manual for the scoring of sleep (Iber, 2007), NREM

sleep can be further classiﬁed into three stages: stage

1 (S1), stage 2 (S2) and SWS. S1 is a transitional

stage from wakefulness to sleep. In S1, alpha rhythms

are attenuated and replaced by low-amplitude, mixed-

frequency activity (4-7 Hz) for more than 50% of the

epoch, coupled with slow eye movements (SEM) and

vertex sharp waves (V waves). S2 is characterized by

sleep spindles with frequencies of 11-16 Hz, and/or

K complexes. Stage SWS is deﬁned by 20% or more

of an epoch consisting of slow wave activity, that is,

waves of frequency 0.5-2 Hz and peak-to-peak ampli-

tude > 75 µ V. In REM sleep, low-amplitude, mixed-

frequency activity (4-7 Hz) similar to that in S1 can

be observed via EEG, in combination with rapid eye

movements and low chin EMG tone. Therefore, sleep

stages can be distinguished from one another by ob-

serving different waveform patterns in EEG, EOG and

EMG.

2.2 PhyCS for Sleep

In addition to assistive technologies for healthy living,

researchers have started to develop PhyCSs that inte-

grate sensing and computing technologies to support

healthy sleep. Choe et al. (2011) conducted large-

scale surveys and interviews to identify the design

opportunities for supporting healthy sleep. Accord-

ing to their study, healthy people care almost as much

about their sleep quality as insomnia patients do. In-

stead of clinical sleep diagnosis based on all-night

PSG recording (including EEG, EOG and EMG), new

portable recording devices with automatic analysis

software have been developed for home applications;

these include Zeo



, Fitbit



, and Bodymedia



. In

addition, a number of phone apps have been devel-

oped to help users analyze their sleep processes (Law-

son et al., 2013). The main purposes of these tech-

nologies are to monitor users sleep quality and to re-

mind them of their sleep problems. Prior study (Ali-

akseyeu et al., 2011) has suggested some interaction

designs for sleep applications, which may help people

to enhance sleep quality, and accommodate the differ-

ing sleep habits of individuals.

2.3 Sleep Monitoring Devices

Recently, many novel techniques for online moni-

toring of physiological signals have been developed

AnEOG-basedSleepMonitoringSystemandItsApplicationonOn-lineSleep-stageSensitiveLightControl

to help patients with sleep disorders (Chandra et al.,

2012). Patients can wear wireless sensors that al-

low caregivers to monitor their conditions and provide

help when needed (Silva et al., 2011). Some prod-

ucts for improving sleep quality have already reached

the market, and these include both sleep-management

systems and sleep clocks (Kay et al., 2012). It is rea-

sonably clear that people have begun to pay particular

attention to their sleep efﬁciency and quality. A sleep-

management system usually consists of one or more

sensors and a monitoring system (or a user interface).

A user wears the sensors on their body and pre-sets

up a wake-up time; the system will then wake up the

user at a proper sleep stage at or before the wake-up

time.

Zeo is a sleep-management product, shaped like

a sports headband with three sensors attached on the

forehead. Fitbit provides the user with a sleep qual-

ity score by measuring how long they sleep and how

many times they wake up. Fitbit also has a silent

wake-up alarm that gently vibrates to wake up the

user by their preset time. The functionalities of Body-

media Fit are similar to those of Fitbit. It lets users

know the quality and efﬁciency of their sleep. In gen-

eral, Zeo, Fitbit and Bodymedia Fit provide users with

helpful information such as sleep efﬁciency (wake-

sleep states) for sleep management; however, they

may not be able to accurately recognize the whole

range of sleep stages.

2.4 EOG-based Sleep Scoring Method

Wearable EOG systems have been used for eye track-

ing in the past. They are easy to use and do not ob-

scure users ﬁeld of view. For example, Bulling et al.

(2009) embedded an EOG system into goggles that

can recognize eye gestures in real time (Bulling et al.,

2009). Manaby and Fukumoto also attempted to de-

sign an all-day-wearable gaze detector based on EOG

(Manabe and Fukumoto, 2006). These systems show

that EOG can potentially be used in our daily life.

Besides eye tracking, Virkkala et al. (2007) fur-

ther proposed that EOG can be utilized to classify

sleep stages effectively. The agreement between com-

puter analysis/scoring of EOG signals, on the one

hand, and the expert scoring of PSG signals is nearly

73%. This is not in the range of inter-score agree-

ment (>82%, Norman et al., 2000), but if its accuracy

can be improved, the EOG-based sleep staging sys-

tem will be a very practicable solution for home-use

sleep monitoring, due to the advantages of comfort-

able recording (as compared to PSG) and complete

sleep staging (as compared to actigraphy).

3 AN EOG-BASED AUTOMATIC

SLEEP SCORING METHOD

Our EOG-based sleep-stage scoring method includes

three parts: preprocessing, feature extraction, and

classiﬁcation. The following subsections introduce

each part in greater detail.

3.1 Preprocessing

The sampling rate of our EOG signals is 256 Hz. Ac-

cording to Rechtschaffen and Kales (1968) (hereafter,

R&K rules), the major brain activity during sleep con-

sists of low-frequency rhythms (< 30 Hz), and there-

fore an eighth-order Butterworth band-pass ﬁlter with

a 0.5-30 Hz pass-band is used to ﬁlter the recordings

for artifact rejection and enhancement of sleep-related

physiological activities. Multi-scale entropy (MSE)

has been used to analyze the ﬁltered signals, as rec-

ommended by (Costa et al., 2005). In addition, an

eighth-order Butterworth band-pass ﬁlter with a 4-8

Hz pass-band is utilized to extract the theta band com-

ponents for the autoregressive (AR) model, as recom-

mended by (Pardey et al., 1996).

3.2 Feature Extraction

Our feature extraction process includes: (a) MSE, (b)

AR modeling, and (c) multi-scale line length (MLL).

The MSE is the principal foundation of the method;

the AR model and the MLL are complementary fea-

tures for increasing the classiﬁcation accuracy of S1

and REM.

a) Multi-scale Entropy. MSE is a signal-analysis

method recently proposed by Costa et al. (2005). It

estimates the complexity associated with the long-

range temporal correlation of a time series. Instead

of using a single scale, MSE measures the complex-

ity of a time series by considering entropy at multi-

ple temporal scales. MSE has been used to analyze

the complexity of various biomedical signals such as

EEG (Kang et al., 2009; Liang et al., 2012; Takahashi

et al., 2009), ECG (Costa et al., 2005), and heart rate

(Costa et al., 2003; Norris et al., 2008).

Given an EOG time series with N samples, x =

{

, x

, ··· , x

}

, the original time series is divided

into non-overlapping time windows of length τ, which

is deﬁned as the scale factor. A coarse-gained time

series y

( j) is then calculated by averaging the data

points inside a time window,

( j) =

jτ

∑

i=( j−1)τ+1

, 1 ≤ j ≤





(1)

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

After obtaining each element of the coarse-gained

time series for each scale τ, the entropy of each

coarse-gained time series is calculated. Theoretically,

if the complexity of the signal is greater, the en-

tropy value will be higher. Relatively, the entropy

value is smaller. Two popular approaches for physi-

ological time series analysis are approximate entropy

(ApEn)(Pincus, 1995) and sample entropy (SampEn)

(Richman and Moorman, 2000). SampEn was pro-

posed to overcome some limitations of ApEn, such as

bias caused by incorrect counting of self-matches to

avoid the occurrence of a natural logarithm of zero in

the calculation. Therefore, in this paper, SampEn has

been utilized to calculate the entropy of the EOG time

series. More details of SampEn can be found in Rich-

man and Moorman (2000). The windows of length

τ are set as 1-8, and therefore we have eight entropy

values corresponding to different time resolutions, ex-

tracted as the features after MSE analysis.

b) Autoregressive Model. An AR model is

a parametric model used to describe a stationary

time series. It is a popular tool for EEG analysis

(Berthomier et al., 2007; Olbrich et al., 2003; Thakor

and Tong, 2004). AR models represent the current

value of a time series x(t) as the weighted sum of its

previous values x(t −i) and an uncorrelated error ε(t),

x(t) =

∑

i=1

x(t − i) + ε(t), (2)

where a(i) is the AR coefﬁcients and p is the order of

the AR model. In this paper, we compute a(i) and p

from the theta band signals (4-8 Hz) extracted by an

eighth-order Butterworth band-pass ﬁlter in the pre-

processing phase. The computed a(i) and p are used

to determine EOG states.

c) Multi-scale line length. MLL calculates the

line length for each coarse-gained time series. The

line length LL of a time series is the sum of the verti-

cal distance (absolute difference) between successive

samples of the time series (Esteller et al., 2001),

LL =

N − 1

N−1

∑

i=1

i+1

− x

|, (3)

where x is the time series considered, i represents the

temporal index of the time series, and N is the total

length of the time series.

Line length reﬂects changes of waveform dimen-

sionality and is a measurement sensitive to variations

of signal amplitude and frequency (Guo et al., 2010).

MLL has the advantage of low computational com-

plexity and is therefore suitable for online applica-

tions. It has also been used for automatic epileptic-

seizure detection in EEG (Esteller et al., 2001).

A total of 24 features, including 13 MSE values,

eight AR coefﬁcients, and three MLL values are ex-

tracted from the EOG signals and fed in to a linear

classiﬁer for sleep-stage classiﬁcation.

3.3 Classiﬁer

Due to its low computational cost, we chose to uti-

lize linear discriminant analysis (LDA) to classify ﬁve

sleep stages based on the extracted MSE values, AR

coefﬁcients and MLL values. In addition, we wanted

to ensure that the proposed EOG features were effec-

tive to a point that sleep stages could be determined

simply using a linear classiﬁer.

a) Linear discriminant analysis. LDA ﬁnds a

hyperplane that best separates two or more classes

of objects or events by adjusting the linear weighting

of their features. Usually, the within-class, between-

class, and mixture scatter matrices are used to for-

mulate the criteria for searching the hyperplane (Lin

et al., 2008; Kuo and Landgrebe, 2004). In order to

test the generalization ability of the proposed method,

the EOG data of 16 subjects were used to train the

LDA classiﬁer, while the EOG data of a different

group of 16 subjects were used to verify the perfor-

mance of our proposed method.

b) Smoothing. Sleep-stage scoring has periodic-

ity and continuity from light to deep (R&K rules). Af-

ter classifying the sleep stage using LDA, some mis-

classiﬁed epochs can be corrected according to tem-

poral contextual information and R&K rules, which

refer to the relation between epochs prior and poste-

rior to the current epoch. For example, three consecu-

tive epochs consisting of S2, REM, and S2 should be

followed by the sequence S2, S2, S2. Similarly, con-

secutive epochs of REM, S1 and REM should be fol-

lowed by the sequence REM, REM, REM. Following

the protocols established by Iber (2007) and Virkkala

et al. (2007)(Iber, 2007; Virkkala et al., 2007), a to-

tal of 10 rules were utilized to smooth the ﬁnal re-

sults and increase the accuracy of our method. Table

1 shows the 10 smoothing rules we followed.

4 SLEEP-STAGE SCORING

EXPERIMENT

4.1 Subjects and Recordings

All-night PSG sleep recordings were obtained from

32 healthy subjects (18 males and 14 females) rang-

ing in age from 18 to 24 years. The subjects were

interviewed about their sleep quality and medical his-

tory. Their sleep efﬁciency ranged from 56% to 97%.

AnEOG-basedSleepMonitoringSystemandItsApplicationonOn-lineSleep-stageSensitiveLightControl

Table 1: List of smoothing rules.

Rule No. Modiﬁcation

1 An REM Epoch before the very ﬁrst

appearance of SWS are replaced with

Wake epochs.

2 Wake, REM, S2 → Wake, S1, S2

3 S1, REM, S2 → S1, S1, S2

4 S2, S1, S2 → S2, S2, S2

5 S2, SWS, S2 → S2, S2, S2

6 S2, REM, S2 → S2, S2, S2

7 SWS, S2, SWS → SWS, SWS, SWS

8 REM, Wake, REM → REM, REM,

REM

9 REM, S1, REM → REM, REM, REM

10 REM, S2, REM → REM, REM, REM

None of them reported any history of neurological

or psychological disorders. The PSG recordings of

each subject were made using six EEG channels (F3-

A2, F4-A1, C3-A2, C4-A1, P3-A2, and P4-A1, fol-

lowing the international 10-20 standard system), two

EOG channels (the above-right and below-left outer

canthus), and a chin EMG channel, and were ac-

quired through the Siesta 802 PSG (Compumedics,

Inc.). The sampling rate was 256 Hz with 16-bit

resolution. The ﬁlter settings of the cut-off frequen-

cies were 0.5-30 Hz for EEG/EOG, and 5-100 Hz for

EMG. These nine-channel signals were used for man-

ual scoring, as suggested by the R&K rules, whereas

only the EOG data were used for the single-channel

sleep-stage scoring system being developed.

The 32 PSG sleep recordings were visually scored

by a sleep specialist using the R&K rules. Each 30-

second epoch was classiﬁed into Wake, REM, S1,

S2, SWS, and movement artifacts. In our exper-

iments, only epochs of the ﬁve sleep stages were

used; epochs of movement artifacts were rejected

(Berthomier et al., 2007; Schaltenbrand et al., 1996).

4.2 Performance Evaluation

Next, we evaluated the performance of our automatic

EOG-based sleep-scoring method. The performance

criterion was the agreement between computer scor-

ing, on the one hand, and expert scoring based on

all PSG channels. The proposed systems sensitivity

corresponding to each sleep stage is shown in Ta-

ble 2. The rows represent the results arrived at by

the experts visual scoring, and the columns represent

the results of our method. The sensitivities of the

proposed automatic stage-scoring method that were

associated with the ﬁve sleep stages were 81.45%

(Wake), 28.05% (S1), 88.12% (S2), 83.06% (SWS)

and 81.05% (REM), yielding an overall sensitivity of

83.33%. The sensitivities for all stages except for

S1 were higher than 81%. S1 can easily be mis-

categorized as any of the other stages except SWS,

and the number of S1 epochs is signiﬁcantly lower

than that of other stages epochs. As such, it is dif-

ﬁcult to create a model with a high sensitivity for

S1. Rosenberg et al. (Rosenberg et al., 2013) report

that inter-scorer agreement in a large group is approx-

imately 83% under current manual scoring rules, a

level similar to that reported for agreement between

expert scorers.

Comparing the recognition results achieved by the

present study against the existing, purely EOG-based

sleep-stage scoring method proposed by (Virkkala

et al., 2007), overall agreement has increased from

73% to 83%. The results of the method in Virkkala

et al. are Wake, 79.7%; S1, 30.6%; S2, 79.7%; SWS,

75.9%; and REM, 75.6%. As detailed in the preced-

ing paragraph, our method performed better in four of

the ﬁve stages (Wake, S2, SWS, and REM), and with

regard to the remaining stage, the results are similar

(28% vs. 30.6%).

5 LIGHTING CONTROL SYSTEM

BASED ON SLEEP STAGES

In addition to sleep quality evaluation, it is worth con-

sidering whether a comfortable sleep monitor can be

utilized to control the sleep environment. Accord-

ingly, the present research also incorporated an active

brightness-control system governed by online moni-

toring of the users sleep stages.

People can now purchase various lighting prod-

ucts that mimic the effect of natural sunlight. For

example, the Philips Wake-up Light



is a dawn-

simulation product that allows users to set up their

wake-up time, the period of dawn or dusk simulation,

and the maximal light intensity. This and other dawn-

dusk simulation products gradually modify light in-

tensity to simulate natural ambient light and help

users fall sleep and/or wake up (Fontana Gasio et al.,

2003; Fromm et al., 2011; Gim

enez et al., 2010).

However, these dawn-dusk simulation products do not

take any account of the users sleep stages. In partic-

ular, since every persons sleep pattern is different and

may vary from time to time, changing light intensity

according to a preset ﬁxed program may not be ap-

propriate, and even disturb a users sleeping partners

who have different bedtime or wake-up time. Hence,

it is desirable to develop an adaptive system that can

dynamically adjust its lighting to let each user sleep

and wake up gradually and individually.

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

Table 2: Confusion matrix of ﬁve-stage classiﬁcation comparing the proposed EOG-based sleep scoring and manual sleep

scoring based on PSG recordings.

EOG system

Wake S1 S2 SWS REM SE(%)

Expert Wake 1094 55 33 11 150 81.45

S1 100 124 76 8 134 28.05

S2 28 203 5880 380 177 88.18

SWS 5 1 376 1884 2 83.06

REM 39 326 116 2 2129 81.05

Overall 83.33

Kappa 0.75

As a separate issue, light is important for safety,

and especially for avoiding falling injuries at night.

According to the American Association of Neurolog-

ical Surgeons (AANS), falling down is the most com-

mon cause of death for people aged 65 or older. Chil-

dren under the age of 4 are also at a high risk of head

injury from falling in and around the home. To reduce

the risk of falls, a function that automatically turns on

appropriate lighting when a user wakes up at midnight

must be considered.

5.1 System Requirements and Design

Concept

In order to extend the PhyCSs-based sleep analysis for

actively controlling the sleep environment, we have

developed a lighting-control system that adaptively

varies its brightness based on the users sleep stage.

Following on from the discussion in the previous sec-

tion, our system was intended fulﬁll the following re-

quirements: a) Use online technology to classify a

users sleep stages; b) Use online technology to ad-

just the lighting of the sleep environment according

to the users sleep stages; c) During hours of darkness,

provide faint light when the user wakes up and moves,

to avoid falling; d) Wake up the user during a proper

sleep stage (i.e. S1, S2 and REM) at or before the

user-speciﬁed wake-up time; and e) Record and pro-

vide sleep information including sleep period, total

sleep time, sleep latency, and sleep efﬁciency so that

the user can learn about their sleep pattern. Figure

1 shows the concept and architecture of our adaptive

light system. The processing steps were as follows:

1. A portable wireless EOG recording unit was used

to record the users sleep EOG online.

2. The EOG signal was sent to a personal computer

via wireless transmission.

3. An automatic sleep-scoring method based on

EOG signals was utilized to classify the users

sleep stage online. The output of the automatic

User

Portable

wireless-EOG

recording system

Automatic sleep

scoring method

Light-control

algorithm

The brightness

of lamplight

EOG Signal

Send EOG Signal to PC

Sleep Stage Information

Light Level

Good Environment

for Sleeping or

Waking up

Figure 1: The concept and architecture of the proposed

adaptive light system.

sleep-scoring method is the users current sleep

stage.

4. According to the users current sleep stage, the

lighting-control algorithm gradually adjusts the

brightness of light. The lighting-control algorithm

also considers the situation of the user moving

about during the night, e.g. to go to the toilet, and

supplies adequate lighting to avoid falls.

5.2 Portable Wireless EOG Recording

Unit

In our design concept, a portable wireless EOG

recording unit (as shown in Figure 2) is integrated

with our sleep-scoring system to perform online

sleep-stage monitoring. It consists of three compo-

nents: Part A is a CC2530 wireless sender; Part B is

the ampliﬁer circuit of electrocardiography; and Part

C is a CC2530 wireless adapter. Because the device

is designed for online automatic sleep-stage scoring,

most of the signals are in the low frequency band.

Therefore, we chose the range of 0.3-35Hz as the

AnEOG-basedSleepMonitoringSystemandItsApplicationonOn-lineSleep-stageSensitiveLightControl

passband of the analog ﬁlter. This system can con-

tinuously operate for, at most, 30 hours.

5.3 Control Module

Our lighting-control module consists of a circuit

board with micro-controller and a sleep-stage-based

lighting-control algorithm to control the brightness of

an LED bulb. The circuit board we used is Arduino

Uno, which has a micro-controller with a 16 MHz

clock rate and 14 digital I/O pins (of which six pro-

vide PWM output). It can be connected to a computer

via a USB cable for both data transmission and power

supply, but it can also be run on a stand-alone basis,

powered via an AC-to-DC adapter. The speciﬁcations

of the LED bulb are, Color temperature: warm white

3500 K; current: 700 mA; voltage: 3.2-3.7v; bright-

ness: 130-150 lm.

Our sleep-stage-based lighting control algorithm

is illustrated in Figure 3. When the user wears our

portable wireless-EOG recording unit and goes to

bed, the brightness decreases from 200 lux to 100

lux over the course of 90 seconds. When the users

sleep stage ﬁrst reaches S1, the brightness decreases

from 100 lux to 50 lux over a period of 150 sec-

onds. Similarly, when users sleep stage enters its ﬁrst

S2, the brightness decreases from 50 lux to 25 lux in

60 seconds. The light is turned off 60 seconds after

users sleep stage enters its ﬁrst SWS epoch. Then, if

the user appears to experience three continuous Wake

epochs, indicating that they are likely to get up, the al-

gorithm turns on the light and the brightness increases

to 50 lux in 5 seconds. When the user goes to sleep

again, the lighting control algorithm would check the

ﬁrst S2 and SWS, and the light is turned off gradually

again. On the other hand, if the users sleep stage does

not show three consecutive Wake epochs, it means

that the user has continued sleeping. Five minutes be-

fore the user-set wake-up time, the lighting control

algorithm checks the users sleep stage. If the users

stage is Wake, the brightness increases to 255 lux in

60 seconds. If, on the other hand, the users stage is

S1, S2, or REM, the brightness increases to 255 lux

in 300 seconds. If there are no Wake, S2, or REM

Figure 2: The three components of the portable wireless-

EOG recording unit.

If first

SWS?

S1,S2,

Rem?

If before

setting

alarm 10

min?

wake?

If before

setting

alarm 5

min?

If first

S2?

If first

S1?

If arousal?

(appears 3

continuous

Wake epochs)

Start the U

I

Lux:0 → 250

Duration: 5min

No-light state No-light state

Lux:0 → 50

Duration: 5s

Lux:0 → 255

Duration: 60s

Lux:25 → 0

Duration: 5s

Lux:50 → 25

Duration: 5s

Lux:100 → 50

Duration: 150s

Lux:200 → 100

Duration: 90s

End

N

Y

N

Sleep Mode

Wake up at

Midnight Mode

Wake up Mode

Figure 3: The ﬂowchart of our light control algorithm,

which has three modes: sleep, wake up at the middle of

the night, and wake up.

stages within the 10 minutes immediately preceding

the wake-up time, the brightness also increases to 255

lux in 300 seconds. The voice alarm rings when the

brightness of the light reaches 255 lux.

6 LIGHTING CONTROL

EXPERIMENT

We recruited three male subjects aged 23 ± 1.1 years

old via the Internet. All three subjects had a habit of

taking a nap at noon. They were asked about their

sleep quality. None of them reported any history of

sleep disorders. They were instructed to keep a regu-

lar sleep-wake schedule for three days prior to the ex-

periment. Subjects were required to abstain from caf-

feine and alcohol throughout the course of the study.

All subjects gave written informed consent before en-

tering the study and were paid for their participation.

The experiment began at about 1:00 PM.

6.1 Procedure

A darkened, quiet room was built for the sleep ex-

periment. A camcorder was set up to record the

experimental process. Two EOG channels, placed

right/above and left/below outer canthus, were con-

nected to our portable wireless EOG recording unit.

The LED blub was placed next to the subjects pil-

low. The total sleep time was 80 minutes for each

subject, this being the usual length of a persons ﬁrst

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

•  1

(e) (f)

Figure 4: Illustration of the actual environment and exper-

imental process. (a)-(e) show the brightness was gradually

decreased when the user transitioned from waking to deep

sleep. (f) shows how the light automatically turned on when

the user woke up.

sleep cycle. Usually, sleep stages are not stable in

the ﬁrst sleep cycle; in particular, they change more

frequently in the ﬁrst sleep cycle than in the later cy-

cles. Therefore, our experimental design focused on

the ﬁrst sleep cycle to verify the stability of the sys-

tem in more difﬁcult cases. Figure 4 shows the exper-

imental environment and process, where (a)-(d) indi-

cate how the brightness was gradually decreased in

the periods when the users sleep stage transited from

wake to light sleep (S1 and S2), and that the light was

turned off in the SWS stage. Figure 4 (e) shows that

the light was automatically turned on when the user

woke up.

6.2 Results

In Figure 5, (a)-(c) show the sleep hypnograms and

light levels for subjects 1, 2, and 3, respectively. The

experiments of all subjects can be deemed successful,

as the light was gradually turned off when their sleep

stage changed from Wake to SWS, and was turned on

when they woke up or were in the light sleep stage 10

minutes before the pre-set wake-up time.

From Figure 5(a), one can observe that brightness

decreased at the beginning of sleep and during the

ﬁrst S1, S2, and SWS stages. The brightness of light

remained zero until 71 minutes into the experiment,

that is, nine minutes before the wake-up time set by

the subject. The sleep stage of Subject 1 at 71 min-

utes was S2. Therefore, the brightness increased to

255 lux over the following 5 minutes. However, some

Wake stages did appear between 30 minutes and 70

minutes, but all were less than three epochs in length.

They may have been caused by body movement with-

out awareness, or by misclassiﬁcation by the sleep-

scoring method. In any case, as these periods were

0 10 20 30 40 50 60 70 min

REM

Wake

SWS

Sleep hypnogram

(A) The sleep hypnogram and brightness of subject no.1

10 20 30 40 50 60 70 min

100

150

200

250

Brightness

Lux

SWS

0 10 20 30 40 50 60 70 min

REM

Wake

SWS

Sleep hypnogram

(B) The sleep hypnogram and brightness of subject no.2

10 20 30 40 50 60 70 min

100

150

200

250

Brightness

Lux

SWS

0 10 20 30 40 50 60 70 min

REM

Wake

SWS

Sleep hypnogram

10 20 30 40 50 60 70 min

100

150

200

250

Brightness

Lux

SWS

Wake

3 Wake

SWS

3 Wake

Wake

3 Wake

Figure 5: The sleep hypnogram and brightness of the three

subjects.

less than three consecutive epochs, the light did not

turn on. The results from Subject 2 were similar to

those of Subject 1.

Figure 5(c) shows that Subject 3 achieved SWS

quickly; however, he woke up two times between the

39-minute mark and the 51-minute mark. Our sys-

tem provided faint light for purposes of safety when

the user woke up, and turned off again when his sleep

stage had returned to SWS. It is worth mentioning that

the sleep stage of Subject 3 changed quickly between

minute 40 and minute 60. Such rapid changes of

sleep stage often result in incorrect sleep-stage scor-

ing. To avoid mistakenly turning on the light when it

is not needed, the lighting control algorithm may be

adjusted according to a users sleep pattern and efﬁ-

ciency. Other factors affecting the sleep environment,

such as music and temperature, can also be consid-

ered in the future.

AnEOG-basedSleepMonitoringSystemandItsApplicationonOn-lineSleep-stageSensitiveLightControl

7 DISCUSSION

Comfortable recording and accurate sleep-stage cas-

siﬁcation are two essential criteria for sensing and

computing technologies designed to support healthy

sleep. Due to their high cost and bulk, conventional

PSG systems are not suitable for sleep recording at

home. Expert scoring of PSG recordings is also a

time-consuming process. Recently developed phone

apps and wearable devices for sleep monitoring are

easy to use, but none claim to accurately recognize

the full range of sleep stages. In this paper, an EOG-

based sleep-scoring system has been proposed. Com-

pared to PSG or EEG recordings, our EOG-based de-

vice has the advantage of easy placement and can be

operated by the individual user with minimal training.

The accuracy of the proposed method as compared

with manual scoring can reach 83.33%. This solution

balances the criteria of comfortable recording and ac-

curate sleep staging.

In addition to sleep-quality evaluation, our sys-

tem incorporates active light control. Our results

demonstrate that light can be adjusted automatically

based on the sleep stages of human subjects. Sleep

hypnograms show that the time-points of different

subjects sleep stages from awake to light sleep or

from light sleep to deep sleep were very different.

Hence, a dawn-dusk simulation should ideally con-

trol the brightness of light based on the users sleep

stage, in order to overcome individual differences in

their sleep patterns.

Prior work (Choe et al., 2011) indicated that users

were not accustomed to wearing biosensors while

asleep. This suggests that we must improve the com-

fort of this type of device in the future. Furthermore,

there is lack of long-term (i.e. month-long or longer)

studies of the use of portable sleep-monitoring de-

vices in daily life (Fontana Gasio et al., 2003). With

improvements to devices and increased user familiar-

ity, negative user experiences can be expected to de-

crease.

Previous studies (Fromm et al., 2011; Gim

enez

et al., 2010) have also suggested that simulated dawn-

dusk light inﬂuences sleep quality. However, the main

purpose of our work is to demonstrate that our method

can adjust the brightness of light automatically based

on users real-time sleep stages. How best to adjust

the light to improve users sleep quality needs further

studies for veriﬁcation.

7.1 Beneﬁts of Adaptive System

Most of the existing work in this area (Lawson et al.,

2013; Kay et al., 2012) only recorded users sleep

stages and helped them to analyze their sleep quality,

without providing an active system to modulate the

sleeping environment appropriately in harmony with

users individual sleep stages.

Kupfer and Reynolds (1997) indicated that televi-

sion was seen as a cause of disrupted sleep (Kupfer

and Reynolds, 1997). However, it may help those

who fear sleeping alone, or who need to be shielded

from outside noise (Aliakseyeu et al., 2011; Choe

et al., 2011). An adaptive system similar to the one

we propose could adjust the brightness and contrast

of TV screens to guide users to sleep, and shut down

the TV automatically when users fall asleep. Since it

has been demonstrated that lights can be adjusted and

turned on and off automatically based on individuals

sleep stages in real time, adaptive lighting adjustment

could also help children who are afraid of the dark. A

sleeping environment that is actively attuned to users

sleep stages will allow them to have a better quality

of sleep.

Besides improving sleep quality, an adaptive sys-

tem might bring other beneﬁts. For example, users

could show their sleep stages to ﬂight attendants on

long air journeys, so that the ﬂight attendants could

avoid disrupting their rest when they are in deep sleep.

For users who sleep with a partner, timely detection

of sleep stages could modulate the sleeping environ-

ment appropriately, for example by adjusting the light

level and TV volume once the partner is asleep. These

automatic control systems need further design work

and veriﬁcation, but are certainly worthy of future re-

search exploration.

7.2 Limitations

Our system still has some limitations. First, new EOG

recording devices that can be easily worn would have

to be developed if extensive use of our system was

to be made. A long-term sleep monitoring system

should be evaluated in the near future. Second, there

is still much scope for improvement of the lighting-

control algorithm, which can and should be ﬁne-tuned

to suit different subjects and scenarios.

8 CONCLUSIONS

This paper proposed a comfortable, accurate EOG-

based sleep-monitoring system. In addition to off-

line sleep quality evaluation, its usefulness extends to

dynamic control of light levels based on users sleep

stages. This study demonstrates the feasibility of us-

ing online and closed-loop PhyCS to control a sleep-

ing environment adaptively.

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

It is hoped that this work may open up new re-

search horizons and strategies with regard to both

sleep monitoring and environmental control. When

a comfortable online sleep monitor is available, this

system can be utilized to control the sleep environ-

ment for easy sleep. A system that automatically and

adaptively adjusts environmental factors based on a

users sleep stages for the purpose of sleep quality en-

hancement is feasible.

ACKNOWLEDGEMENTS

This work was supported by the National Science

Council of Taiwan under Grants NSC 102-2221-

E-009-082-MY3, 100-2410-H-006-025-MY3, and

1102-2220-E-006-001. Moreover, this paper was also

supported by ”Aiming for the Top University Pro-

gram” of the National Chiao Tung University and

Ministry of Education,Taiwan, R.O.C.

REFERENCES

Aliakseyeu, D., Du, J., Zwartkruis-Pelgrim, E., and Sub-

ramanian, S. (2011). Exploring interaction strate-

gies in the context of sleep. In Human-Computer

Interaction–INTERACT 2011, pages 19–36. Springer.

Bauer, J., Consolvo, S., Greenstein, B., Schooler, J., Wu,

E., Watson, N. F., Kientz, J., and Bauer, J. S. (2012).

Shuteye: encouraging awareness of healthy sleep rec-

ommendations with a mobile, peripheral display. In

Proceedings of the 2012 ACM annual conference on

Human Factors in Computing Systems, pages 1401–

1410. ACM.

Berthomier, C., Drouot, X., Herman-Sto

ıca, M.,

Berthomier, P., Prado, J., Bokar-Thire, D., Benoit, O.,

Mattout, J., and d’Ortho, M.-P. (2007). Automatic

analysis of single-channel sleep eeg: validation in

healthy individuals. Sleep, 30(11):1587.

Bulling, A., Roggen, D., and Tr

oster, G. (2009). Wearable

EOG goggles: eye-based interaction in everyday en-

vironments. ACM.

Chandra, H., Oakley, I., and Silva, H. (2012). Designing to

support prescribed home exercises: understanding the

needs of physiotherapy patients. In Proceedings of the

7th Nordic Conference on Human-Computer Interac-

tion: Making Sense Through Design, pages 607–616.

ACM.

Choe, E. K., Consolvo, S., Watson, N. F., and Kientz, J. A.

(2011). Opportunities for computing technologies to

support healthy sleep behaviors. In Proceedings of the

SIGCHI Conference on Human Factors in Computing

Systems, pages 3053–3062. ACM.

Costa, M., Goldberger, A. L., and Peng, C.-K. (2005). Mul-

tiscale entropy analysis of biological signals. Physical

Review E, 71(2):021906.

Costa, M., Peng, C.-K., L Goldberger, A., and Hausdorff,

J. M. (2003). Multiscale entropy analysis of human

gait dynamics. Physica A: Statistical Mechanics and

its applications, 330(1).

Esteller, R., Echauz, J., Tcheng, T., Litt, B., and Pless, B.

(2001). Line length: an efﬁcient feature for seizure

onset detection. In Engineering in Medicine and Biol-

ogy Society, 2001. Proceedings of the 23rd Annual In-

ternational Conference of the IEEE, volume 2, pages

1707–1710. IEEE.

Fontana Gasio, P., Kr

auchi, K., Cajochen, C., Someren,

E. v., Amrhein, I., Pache, M., Savaskan, E., and

Wirz-Justice, A. (2003). Dawn–dusk simulation

light therapy of disturbed circadian rest–activity cy-

cles in demented elderly. Experimental gerontology,

38(1):207–216.

Fromm, E., Horlebein, C., Meergans, A., Niesner, M., and

Randler, C. (2011). Evaluation of a dawn simulator

in children and adolescents. Biological Rhythm Re-

search, 42(5):417–425.

Gim

enez, M. C., Hessels, M., van de Werken, M., de Vries,

B., Beersma, D. G., and Gordijn, M. C. (2010). Ef-

fects of artiﬁcial dawn on subjective ratings of sleep

inertia and dim light melatonin onset. Chronobiology

International, 27(6):1219–1241.

Guo, L., Rivero, D., Dorado, J., Rabunal, J. R., and Pa-

zos, A. (2010). Automatic epileptic seizure detec-

tion in eegs based on line length feature and artiﬁcial

neural networks. Journal of neuroscience methods,

191(1):101–109.

Iber, C. (2007). The aasm manual for the scoring of sleep

and associated events: rules, terminology and techni-

cal speciﬁcations.

Kang, X., Jia, X., Geocadin, R. G., Thakor, N. V., and

Maybhate, A. (2009). Multiscale entropy analysis of

eeg for assessment of post-cardiac arrest neurological

recovery under hypothermia in rats. Biomedical Engi-

neering, IEEE Transactions on, 56(4):1023–1031.

Kay, M., Choe, E. K., Shepherd, J., Greenstein, B., Watson,

N., Consolvo, S., and Kientz, J. A. (2012). Lullaby:

a capture & access system for understanding the sleep

environment. In Proceedings of the 2012 ACM Con-

ference on Ubiquitous Computing, pages 226–234.

ACM.

Kuo, B.-C. and Landgrebe, D. A. (2004). Nonparametric

weighted feature extraction for classiﬁcation. Geo-

science and Remote Sensing, IEEE Transactions on,

42(5):1096–1105.

Kupfer, D. J. and Reynolds, C. F. (1997). Management

of insomnia. New England Journal of Medicine,

336(5):341–346.

Lawson, S., Jamison-Powell, S., Garbett, A., Linehan, C.,

Kucharczyk, E., Verbaan, S., Rowland, D. A., and

Morgan, K. (2013). Validating a mobile phone appli-

cation for the everyday, unobtrusive, objective mea-

surement of sleep. In Proceedings of the SIGCHI

Conference on Human Factors in Computing Systems,

pages 2497–2506. ACM.

Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., and Wang,

Y.-H. (2012). Automatic stage scoring of single-

AnEOG-basedSleepMonitoringSystemandItsApplicationonOn-lineSleep-stageSensitiveLightControl

channel sleep eeg by using multiscale entropy and au-

toregressive models. Instrumentation and Measure-

ment, IEEE Transactions on, 61(6):1649–1657.

Lin, C.-T., Ken-Li, L., Li-Wei, K., Sheng-Fu, L., Bor-Chen,

K., et al. (2008). Nonparametric single-trial eeg fea-

ture extraction and classiﬁcation of driver’s cognitive

responses. EURASIP Journal on Advances in Signal

Processing, 2008.

Manabe, H. and Fukumoto, M. (2006). Full-time wear-

able headphone-type gaze detector. In CHI’06 ex-

tended abstracts on Human factors in computing sys-

tems, pages 1073–1078. ACM.

Norman, R. G., Pal, I., Stewart, C., Walsleben, J. A., and

Rapoport, D. M. (2000). Interobserver agreement

among sleep scorers from different centers in a large

dataset. Sleep, 23(7):901–908.

Norris, P. R., Anderson, S. M., Jenkins, J. M., Williams,

A. E., and Morris Jr, J. A. (2008). Heart rate multi-

scale entropy at three hours predicts hospital mortality

in 3,154 trauma patients. Shock, 30(1):17–22.

Olbrich, E., Achermann, P., and Meier, P. (2003). Dynamics

of human sleep eeg. Neurocomputing, 52:857–862.

Pardey, J., Roberts, S., and Tarassenko, L. (1996). A review

of parametric modelling techniques for eeg analysis.

Medical engineering & physics, 18(1):2–11.

Pincus, S. (1995). Approximate entropy (apen) as a com-

plexity measure. Chaos: An Interdisciplinary Journal

of Nonlinear Science, 5(1):110–117.

Rechtschaffen, A. and Kales, A. (1968). A manual of stan-

dardized terminology, techniques and scoring system

for sleep stages of human subjects.

Richman, J. S. and Moorman, J. R. (2000). Physiolog-

ical time-series analysis using approximate entropy

and sample entropy. American Journal of Physiology-

Heart and Circulatory Physiology, 278(6):H2039–

H2049.

Rosenberg, R. S., Van Hout, S., et al. (2013). The ameri-

can academy of sleep medicine inter-scorer reliability

program: sleep stage scoring. Journal of clinical sleep

medicine: JCSM: ofﬁcial publication of the American

Academy of Sleep Medicine, 9(1):81–87.

Schaltenbrand, N., Lengelle, R., Toussaint, M., Luthringer,

R., Carelli, G., Jacqmin, A., Lainey, E., Muzet, A.,

Macher, J.-P., et al. (1996). Sleep stage scoring us-

ing the neural network model: comparison between

visual and automatic analysis in normal subjects and

patients. Sleep, 19(1):26.

Silva, H., Palma, S., and Gamboa, H. (2011). Aal+: Con-

tinuous institutional and home care through wireless

biosignal monitoring systems. In Handbook of Digi-

tal Homecare, pages 115–142. Springer.

Stepanski, E. J. and Wyatt, J. K. (2003). Use of sleep hy-

giene in the treatment of insomnia. Sleep medicine

reviews, 7(3):215–225.

Takahashi, T., Cho, R. Y., Murata, T., Mizuno, T., Kikuchi,

M., Mizukami, K., Kosaka, H., Takahashi, K., and

Wada, Y. (2009). Age-related variation in eeg com-

plexity to photic stimulation: A multiscale entropy

analysis. Clinical Neurophysiology, 120(3):476–483.

Thakor, N. V. and Tong, S. (2004). Advances in quantitative

electroencephalogram analysis methods. Annu. Rev.

Biomed. Eng., 6:453–495.

Virkkala, J., Hasan, J., V

arri, A., Himanen, S.-L., and

uller, K. (2007). Automatic sleep stage classiﬁca-

tion using two-channel electrooculography. Journal

of neuroscience methods, 166(1):109–115.

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems