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 (wakefulness W 0 and deep sleep N 3 or slow wave sleep SWS). In SWS, 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 during N 3 deep 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).
Highlights
The manual scoring of sleep patterns is a time-consuming process, consisting of the determination of sleep states using an electroencephalograph (EEG) signal
✓ Hidden layer neurons; 2(min) ≤ hidden layer neurons (HN) ≤ 28(max) ✓ Learning rate (α) = 0.1 ✓ Set up the target vector which specifies the target class of each pattern in the training set ✓ Display update rate = 100 ✓ Arrange the input patterns of the training set as one-dimensional columns in an array (P) ✓ Number of training epochs (EP) = 2,500
This paper considered a learning vector quantization (LVQ)-neural network (NN)- and support vector machine (SVM)-based automatic classification algorithm for 40 Hz Auditory steady state response (ASSR) ensemble averaged signals
Summary
The manual scoring of sleep patterns is a time-consuming process, consisting of the determination of sleep states using an electroencephalograph (EEG) signal. This is followed by a description of data extraction in the form of an ASSR ensemble of averaged sweep signals from EEG generated for classification. Brignol et al [43] proposed a phase space-based algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of this approach was demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3 to 30-s.
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