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Toward a Taxonomy of Autonomic Sleep Patterns with Electrodermal Activity

Explore how Electrodermal Activity (EDA) reveals unique sleep patterns. Analyze EDA storms during sleep, distinguish stages, and compare with traditional methods. Discover insights into sleep quality and disorders.

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Toward a Taxonomy of Autonomic Sleep Patterns with Electrodermal Activity

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  1. 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

  2. What is Electrodermal Activity? • Electrical measures of sweat gland activity • Index of sympathetic nervous activation • Classically, has been measured with wired and gelled electrodes on the skin • Our research group developed a dry electrode, wearable sensor for long-term ambulatory measurement

  3. 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?

  4. Measurement of Sleep Polysomnography (PSG) + measures EEG and more, provides 30 s epochs labeled as: Wake, NonREM (stage 1-3), and REM - expensive and obtrusive Actigraphy + less invasive than PSG, low cost - only measures movement Our EDA sensor + comfortable, same or lower cost than actigraphy + measures EDA, skin temperature and actigraphy -+ measures different patterns than traditional

  5. Objectives • Evaluate EDA sleep patterns quantitatively from healthy groups • Understand what the changing patterns ofEDA mean in terms of traditional PSG. Experiments Collected EDA+motionduring sleep from healthy adults Total: 168nights

  6. Analysis: sleep vs. wake Sleep and wake are discriminated from accelerometer data with standard zero-crossing and Cole’s function wake

  7. 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 peaks Storm 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 Raw EDA * EDA Storm

  8. EDA vs. sleep stages from PSG EDA raw data Motion data EDA peaks Sleep Stage Wake is red

  9. 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.

  10. 2/3 of nights had >= 1 storm 1/2 of nights had >= 2 storms Histogram of # of storms over night (168 nights)

  11. Summary • We 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 Steps • Needs more detail analysis with EEG and heart rates • Are they related with sleep quality/ Sleep disorders or Memory consolidation?

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