Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Abstract

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

“…Table 3). Those two classes are prone to be misclassified while staging, with S1 normally exhibiting the poorest classification performances [32].…”

“…Therefore it is well accepted by the scientific community that one EEG channel shall sufficiently provide information to classify the sleep stages as stated in [11], and also explored in [5] and [32]. The recordings analyzed here are formatted in the EDF standard and contain two EEG channels, Pz-Oz and Fpz-Cz, sampled at 100Hz.…”

“…As the sleep staging with EEG signals is considered a typical problem of classification with extracted features [32,4,25], selection of the classifier inputs is the most important component of pattern classification since even the best classifier will perform poorly if the inputs are not selected well [35]. Moreover, it can be observed that different classifiers provide distinct performances for the same feature set, indicating that matching both may enhance results.…”

EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

“…Figure 3 shows a topographic comparison of the brains of 10 subjects with two different stimuli. As can be seen from Figure 3, some subjects, such as subjects 1,3,4,6,7,8, and 10, have certain differences in electrical activity produced in the frontal area of the brain. Additionally, the brain signal energy of the prefrontal area was obviously strong when the subject responded to self-photos, but it did not change much when the subject responded to non-self photos.…”

“…Therefore, EEG signals serve as a highly safe identifier with respect to personal authentication [6,7]. Numerous brain and psychological studies have used EEGs in order to study the neural activity underlying different emotional and psychological phenomena [8,9].…”