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

Abstract

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 Stage 1 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, Stage 1 + 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 staging using state machine-controlled decision trees

“…Some of the algorithms proposed recently include the use of support vector machines [4]- [6], hidden Markov models [7] and frequency coupling with linear discriminant analysis classification [8]. Other methods have also used artificial neural networks [2], [9], [10] and decision trees [11], [12] for classification with a variety of time, frequency, entropy [13], [14] and wavelet [10] sleep data either recorded by the researchers or using signals available from public sleep databases. It is generally difficult to compare the performance of algorithms that have been evaluated using different databases.…”

Statistical features-based comparison of analysis and synthesis of normal and epileptic electroencephalograms for various wavelets

“…Over the length of a signal, variable window size can be applied in the wavelet transform (WT). Depending on the signal specifications, this allows the wavelet to get stretched or compressed [6,7]. This makes WT a very popularly used feature extraction technique for nonstationary signals, such as EEGs [8,9].…”

“…The amplitude, frequency, and characteristics of an EEG signal change from one state to the other, and with age. An EEG is decomposed mainly into 5 subbands: delta (0-4 Hz), occurring during deep sleep, during childhood, and in serious organic brain diseases; theta (4)(5)(6)(7)(8), occurring in childhood, during emotional stress; alpha (8)(9)(10)(11)(12)(13), occurring in a normal person in an awake state; beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), affected by mental activity; and gamma (above 30 Hz) [1,2]. Instead of studying the complete EEG, EEG subband-related information yields more accurate information about the underlying neuronal activities [3].…”

Efficient sleep stage classification based on EEG signals

“…NREM sleep is further separated into 4 stages in which the eyes are usually closed and many nervous centers are inactive, so the brain awareness completely or partially loses consciousness and becomes a less complex system. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders [3].…”