title: 40-Hz ASSR fusion classification system for observing sleep patterns.
abstract: This paper presents a fusion-based neural network (NN) classification algorithm for 40-Hz auditory steady state response (ASSR) ensemble averaged signals which were recorded from eight human subjects for observing sleep patterns (wakefulnessW0and deep sleepN3or slow wave sleepSWS). InSWS, sensitivity to pain is the lowest relative to other sleep stages and arousal needs stronger stimuli. 40-Hz ASSR signals were extracted by averaging over 900 sweeps on a 30-s window. Signals generated duringN3deep sleep state show similarities to those produced when general anesthesia is given to patients during clinical surgery. Our experimental results show that the automatic classification system used identifies sleep states with an accuracy rate of 100% when the training and test signals come from the same subjects while its accuracy is reduced to 97.6%, on average, when signals are used from different training and test subjects. Our results may lead to future classification of consciousness and wakefulness of patients with 40-Hz ASSR for observing the depth and effects of general anesthesia (DGA).
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada.
authors.forename: Gulzar A
authors.initials: GA
authors.lastname: Khuwaja
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada.
authors.forename: Sahar Javaher
authors.initials: SJ
authors.lastname: Haghighi
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, 40 St. George Street, Toronto, ON M5S 2E4 Canada.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Hatzinakos
keywords: ASSR extraction; Adaptive classification; Depth of general anesthesia (DGA); Features-level fusion; Observing sleep patterns