An Accurate Sleep Stages Classification Method Based on State Space Model

Abstract

The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a statespace based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

“…In the classification of two classes, Abdulla et al [ 6 ] has the highest accuracy of 93%; however, the number of epoch they used is only 23806. The sensitivity of S1 in this paper is 19.32%, which is higher than 18.3% of Ghimatgar [ 7 ] and 15.9% of Shen [ 27 ].…”

“…As can be seen from the Table 30 above, when the only DSSMFs is used, the method proposed in this paper has a certain improvement in accuracy compared with the others. After adding LEFs on the basis of DSSMFs, the classification accuracies of two to six classes are improved by 1.27%, 1.02%, 1.27%, 1.38% and 0.72% compared with our previous study [ 27 ].…”

“…It can be seen from Table 32 that when a large number of samples are used, the accuracy is also improved compared with other published methods. Among them, the accuracy for the classification of four classes is 93.87%, while the Sharma [ 28 ] is 92.1% and the Shen [ 27 ] is 93.0%. In the classification of two classes, Abdulla et al [ 6 ] has the highest accuracy of 93%; however, the number of epoch they used is only 23806.…”

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

“…The AASM defines five different stages of sleep (wake, N1, N2, N3, and REM), whereas the previous R&K guidelines defined seven stages (wake, S1, S2, S3, S4, and REM). Of all the reviewed articles, only 21 performed classification of all the sleep stages defined by either of these guidelines [ 24 , 39 , 42 , 45 , 50 , 58 , 67 , 72 , 76 , 80 , 88 , 89 , 90 , 93 , 101 , 102 , 105 , 108 , 109 , 117 , 121 ]. Amongst them, all but three [ 24 , 58 , 150 ] used EEG signals for classification, where the difference between sleep stages is known to be most obvious.…”

Sleep Stage Classification for Prediction of Human Sleep Disorders by Using Machine Learning Approach

“…to select best features. For classification purpose Random Forest Algorithm is used.A method introduced by Huaming Shen et al[9] is state space based sleep stage classification. This model consists of two phases: first is offline training phase and second is identification phase.…”