Recognition of Sleep Dependent Memory Consolidation with Multi-modal Sensor Data
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Picard_Recognition of sleep.pdf
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Author(s)
Sano, Akane
Picard, Rosalind W.
Date Issued
May 2013
Journal
Proceedings of the 2013 IEEE International Conference on Body Sensor Networks
Publisher
Institute of Electrical and Electronics Engineers
Citation
Sano, Akane, and Rosalind W. Picard. "Recognition of Sleep Dependent Memory Consolidation with Multi-modal Sensor Data." In 2013 IEEE International Conference on Body Sensor Networks (BSN), 6-9 May 2013, Cambridge, MA, USA.Pp. 1–4. IEEE.
Version
Author's final manuscript
Abstract
This paper presents the possibility of recognizing sleep dependent memory consolidation using multi-modal sensor data. We collected visual discrimination task (VDT) performance before and after sleep at laboratory, hospital and home for N=24 participants while recording EEG (electroencepharogram), EDA (electrodermal activity) and ACC (accelerometer) or actigraphy data during sleep. We extracted features and applied machine learning techniques (discriminant analysis, support vector machine and k-nearest neighbor) from the sleep data to classify whether the participants showed improvement in the memory task. Our results showed 60–70% accuracy in a binary classification of task performance using EDA or EDA+ACC features, which provided an improvement over the more traditional use of sleep stages (the percentages of slow wave sleep (SWS) in the 1st quarter and rapid eye movement (REM) in the 4th quarter of the night) to predict VDT improvement.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/BSN.2013.6575479
