Subject: Performance of a Wireless Dry Sensor System in Automatically Monitoring Sleep and Wakefulness
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Performance of a Wireless Dry Sensor System in Automatically

Monitoring Sleep and Wakefulness

Stephan E Fábregas1, Jack Johnstone2, John R Shambroom1

1

2

1Zeo, Inc, Newton, MA, USA 2Valley Sleep Center, Burbank, CA, USA

Introduction:

The ability to wirelessly monitor and track sleep and wakefulness

in a way that is non-invasive and inexpensive could be of utility in

many areas including research medicine and education One

Results:

Mean sleep/wake measurements between the three systems appeared to be similar. The WS is designed to

accurately detect wake periods that last for at least 2 minutes. Therefore, number of awakenings is reported as

awakenings lasting at least 4 continuous epochs

many areas, including research, medicine, and education. One

system with the potential to effectively accomplish this has been

developed, which is composed of a single channel no-prep dry

fabric sensor that rests on the forehead through the use of a

headband. Data are then wirelessly transmitted to an

accompanying base station and processed through a neural

network to determine periods of sleep and wakefulness

awakenings lasting at least 4 continuous epochs.

Sleep and Wake Summary Measures

WS

T1

T2

ACT

Latency to Persistent Sleep

10 continuous minutes

LPS (min±SEM)

31.8±16.1

33.0±16.5

36.4±16.3

34.7±15.8

Number of Awakenings

Occurring after onset of persistent sleep

NA (#±SEM)

2.6±0.7

2.2±0.4

2.2±0.4

3.3±0.6

network to determine periods of sleep and wakefulness.

This study was conducted to assess the accuracy of derived

measures of this wireless system (WS) and Actigraphy (ACT) in

comparison to polysomnography (PSG).

Methods:

Participants:

g

p

p

and lasting at least 2 continuous minutes

(

)

Total Sleep Time

TST (min±SEM)

312.8±17.8 303.3±17.7 296.8±16.7 313.2±13.9

Wake After Sleep Onset

WASO (min±SEM) 41.7±13.4

27.8±3.7

47.1±8.5

48.5±9.7

Sleep Efficiency

SE (%±SEM)

86.7±3.4

84.5±4.0

82.6±3.6

86.4±2.4

Sensitivity and specificity to sleep were

Participants:

•10 adults (4 female)

•33.7 years old (±10.7,SD)

•No sleep complaints

Study Protocol:

•Sleep in the laboratory at the participant’s habitual bedtime

•Concurrent measurement of PSG, WS, and ACT

Sensitivity and specificity to sleep were

higher in the WS than in ACT when

compared to PSG. Inter-rater agreement

of sleep/wake between the WS and PSG

was moderate-to-high, while agreement

between ACT and PSG was moderate-to-

low

Sleep Sensitivity Sleep Specificity Accuracy Cohen's kappa

WS v T1

96.2%

72.9%

90.7%

0.63

WS v T2

95.9%

69.1%

88.4%

0.57

ACT v T1

92.8%

65.1%

84.8%

0.41

ACT v T2

93.5%

65.0%

84.6%

0.43

As an example of a typical night from this data set, below is a representation of one subject’s

(35F) night of sleep.

Concurrent measurement of PSG, WS, and ACT

•PSG data collected with Cadwell Easy II PSG, sampled at 200

samples per second

•WS data were sampled at 128 samples per second

•ACT data were collected on a Mini-Mitter Actiwatch 64, epoch

length set at 30 seconds

•Sleep records were scored by 2 trained technicians (T1 and T2)

low.

LPS

(min)

NA

(#)

TST

(min)

WASO

(min)

SE

(%)

WS

42

4

249

44

75

Stages 3 and 4

Wake

Stages 1 and 2

REM

Sleep

p

y

(

)

according to Rechtschaffen & Kales

•Sleep records were scored automatically by the WS via its neural

network

•ACT records were scored automatically by Actiware 5.0 software

at medium wake threshold sensitivity (wake threshold value of

40 activity counts)

T1

40

3

263

31

79

T2

41

2

265

28

80

ACT

37

5

290

38

87

•6 subjects contributed two nights of recordings and 4 subjects

one night, resulting in 16 total records

•Inter-rater agreement was performed on summary statistics of

sleep parameters on a 30-second epoch-by-epoch basis for

agreement/disagreement of sleep/wake decisions.

Support: This research was funded by Zeo, Inc, Newton, MA, USA.

Conclusion:

The wireless system shows promise as an effective way to distinguish wake and sleep during the night and has

potential as a cost-effective ambulatory system that can distinguish sleep and wake, while also providing sleep stage

information over the course of nighttime recordings.