Real Time Sleep Detection System Using New Statistical Features of the Single EEG Channel
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Authors
Aboalayon, Khald A.I.
Faezipour, Miad
Issue Date
2017-03-24
Type
Presentation
Language
en_US
Keywords
Automatic sleep stage classification , Detection , Electroencephalography (EEG)
Alternative Title
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. Many of the prior and current related studies use multiple EEG channels, and are based on 30s or 20s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, the aim of this work is to present a novel and efficient real time 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. First, we run our algorithm off line using the PhysioNet Sleep European Data Format (EDF) Database to classify six sleep stages. 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. Second, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance. Finally, we propose an effective EEG classification technique for detecting sleep to only prove that our algorithm is simple and works fast in real time in an efficient way using Neurosky Mindwave headset that gathers the user’s brain waves.