Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States

Authors:
Rakesh Kumar Sinha
Affiliations:
Department of Biomedical Instrumentation, Birla Institute of Technology, Ranchi, India 835215
Venue:
Journal of Medical Systems
Year:
2008

Citing 10
Cited 6

Introduction to artificial neural systems

Introduction to artificial neural systems
Neural network model: application to automatic analysis of human sleep

Computers and Biomedical Research
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs

Journal of Medical Systems
Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks
Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals

Journal of Medical Systems
Automatic recognition of vigilance state by using a wavelet-based artificial neural network

Neural Computing and Applications
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Journal of Medical Systems
Backpropagation Artificial Neural Network Classifier to Detect Changes in Heart Sound due to Mitral Valve Regurgitation

Journal of Medical Systems
Neural Network-Based Diagnosing for Optic Nerve Disease from Visual-Evoked Potential

Journal of Medical Systems
Wavelet/mixture of experts network structure for EEG signals classification

Expert Systems with Applications: An International Journal

Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis

Journal of Medical Systems
Evaluation of Flow---Volume Spirometric Test Using Neural Network Based Prediction and Principal Component Analysis

Journal of Medical Systems
Review article: Human scalp EEG processing: Various soft computing approaches

Applied Soft Computing
Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Neurocomputing
Automatic classification of sleep stages based on the time-frequency image of EEG signals

Computer Methods and Programs in Biomedicine
Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Backpropagation artificial neural network (ANN) has been designed to classify sleep---wake stages. Four hours continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) from conscious subjects were digitally recorded and stored in computer. EOG and EMG signals were used for manual identification of sleep states before training and testing of ANN. The percentages power of the 2 s epochs of the digitized EEG signals from each of three sleep---wake patterns, sleep spindles (SS), rapid eye movement (REM) sleep and awake (AWA) sates, were calculated and analyzed to select the manually confirmed sleep---wake states for each epoch. Further, second order Daubechies mother wavelet has been used to get the wavelet coefficients for the selected EEG epochs. The wavelet coefficients for the EEG epochs (64 data) were selected as inputs for the training the network and to classify SS, REM sleep and AWA stages. The ANN architecture used (64---14---3) in present study shows overall very good agreement with manual sleep stage scoring with an average of 95.35% for all the 1,140 samples tested from SS, REM and AWA stages. This architecture of ANN was also found effectively differentiating the EEG power spectra from different sleep---wake states (96.84% in SS, 93.68% in REM sleep, 95.52% in AWA state). The high performance observed with the system based on wavelet coefficients along with the ANN, highlights the need of this computational tool into the field of sleep research.