@inproceedings{3edaeabd364a4ec0876415cf23e04f70,
title = "Recognition of sleep dependent memory consolidation with multi-modal sensor data",
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 N24 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.",
keywords = "actigraphy, classification, EDA, EEG, galvanic skin response, GSR, memory consolidation, visual discrimination task (VDT)",
author = "Akane Sano and Picard, {Rosalind W.}",
year = "2013",
doi = "10.1109/BSN.2013.6575479",
language = "English (US)",
isbn = "9781479903306",
series = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013",
booktitle = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013",
note = "2013 IEEE International Conference on Body Sensor Networks, BSN 2013 ; Conference date: 06-05-2013 Through 09-05-2013",
}