In this section
@inproceedings{Imtiaz:2015:10.1109/EMBC.2015.7318378,
author = {Imtiaz, SA and Rodriguez-Villegas, E},
doi = {10.1109/EMBC.2015.7318378},
pages = {378--381},
publisher = {IEEE},
title = {Automatic sleep staging using state machine-controlled decision trees.},
url = {http://dx.doi.org/10.1109/EMBC.2015.7318378},
year = {2015}
}
TY - CPAPER
AB - Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.
AU - Imtiaz,SA
AU - Rodriguez-Villegas,E
DO - 10.1109/EMBC.2015.7318378
EP - 381
PB - IEEE
PY - 2015///
SN - 1557-170X
SP - 378
TI - Automatic sleep staging using state machine-controlled decision trees.
UR - http://dx.doi.org/10.1109/EMBC.2015.7318378
UR - http://hdl.handle.net/10044/1/29343
ER -