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Toward a Taxonomy of Autonomic Sleep Patterns with Toward a Taxonomy of Autonomic Sleep Patterns with

Toward a Taxonomy of Autonomic Sleep Patterns with - PowerPoint Presentation

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Toward a Taxonomy of Autonomic Sleep Patterns with - PPT Presentation

Electrodermal Activity Akane Sano and Rosalind W Picard Massachusetts Institute of Technology Media Lab Affective Computing Group akanesmitedu What is Electrodermal Activity Electrical measures of sweat gland ID: 293944

eda sleep nights storms sleep eda storms nights storm wake patterns measures activity electrodermal psg night analysis data healthy

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Slide1

Toward a Taxonomy of Autonomic Sleep Patterns with Electrodermal Activity

Akane

Sano and Rosalind W. Picard,

Massachusetts Institute of Technology

Media Lab

Affective Computing Group

akanes@mit.edu Slide2

What is

Electrodermal Activity?

Electrical measures of sweat gland

activity

Index of sympathetic nervous activationClassically, has been measured with wired and gelled electrodes on the skinOur research group developed a dry electrode, wearable sensor for long-term ambulatory measurementSlide3

Electrodermal Activity (EDA) during sleep

Q:Sympathetic nervous activity goes up during a day and goes down and get silent during sleep?

A: No!!!

High frequency “storm” patterns during sleep

Why these storm patterns happen?Slide4

Measurement of Sleep

Polysomnography (PSG)

+ measures EEG and more, provides 30 s epochs labeled as: Wake,

NonREM (stage 1-3), and REM - expensive and obtrusiveActigraphy + less invasive than PSG, low cost - only measures movementOur EDA sensor + comfortable, same or lower cost than

actigraphy

+ measures EDA, skin temperature and

actigraphy

-+ measures different patterns than traditionalSlide5

ObjectivesEvaluate

EDA sleep

patterns quantitatively from

healthy

groupsUnderstand what the changing patterns of EDA mean in terms of traditional PSG.

Experiments

Collected

EDA+motion

during

sleep from healthy adults

Total:

168

nightsSlide6

Analysis: sleep vs. wake

Sleep and wake are discriminated from accelerometer data with standard

zero-crossing and

Cole’s function

wakeSlide7

Analysis: EDA storms during sleep

After

low-pass filtering (0.4

Hz, 32nd order FIR filter

), we detected “storm” regions during sleep, regions of EDA with a burst of peaksStorm epoch: > 3

peaks /

30-sec with

the slope of each peak

>

0.09 micro Siemens/s Storm

: Storm epochs that are adjacent or within 5 minutes of each other Example: 6 storms in one night of sleep

*

* wake

EDA Storm

Raw EDASlide8

EDA vs. sleep stages from PSG

EDA raw data

Motion data

EDA peaks

Sleep Stage

Wake is redSlide9

More than 90 % of EDA Storms occurred in SWS and NREM2 (N=7, one night each)

One subject had storms below the

threshold

Portion of storm epochs in each category of sleep.Slide10

Histogram of # of storms over night (168 nights)

2/3 of nights had >= 1 storm

1/2 of nights had >= 2 stormsSlide11

SummaryWe analyzed electrodermal activity

from healthy subjects over

150 nights

More than 90 % of EDA storms occurred in SWS and NREM2 (N=7, one night each)

2/3 of nights showed more than 1 EDA storm 1/2 of nights had more than 2 storms Next StepsNeeds more detail analysis with EEG and heart ratesAre they related with sleep quality/ Sleep disorders or Memory consolidation?