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

An ensemble system for automatic sleep stage classification using single channel EEG signal

Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 ± 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 ± 4.0%.

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients

Sleep scoring using artificial neural networks

In this paper we present a new method to do automatic sleep stage classification. The algorithm consists of basically three modules. A wavelet packet transformation (WPT) is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, a feature generator which quantifies the information and reduce the data set size, and an artificial neural network for doing optimal classification. The classification results compared to those of a human expert reached a 70 to 80% of agreement.

http://http.icsi.berkeley.edu/ftp/global/pub/techreports/1999/tr-99-008.pdf

Edgar Oropesa

Papers

Sleep Stage Classification using Wavelet Transform and Neural Network