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An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features
- Source :
- Sensors, Volume 20, Issue 17, Sensors (Basel, Switzerland), Sensors, Vol 20, Iss 4677, p 4677 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen&rsquo<br />s and Kale&rsquo<br />s (R&amp<br />K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.
- Subjects :
- Stage classification
medicine.medical_specialty
Computer science
02 engineering and technology
Electroencephalography
lcsh:Chemical technology
Biochemistry
Sleep medicine
Article
Analytical Chemistry
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
medicine
lcsh:TP1-1185
EEG
Electrical and Electronic Engineering
Instrumentation
medicine.diagnostic_test
business.industry
sleep stage
Pattern recognition
Atomic and Molecular Physics, and Optics
wavelet packet
state space model
020201 artificial intelligence & image processing
Artificial intelligence
business
Classifier (UML)
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....166dfe6a461edcdce8d52fbae5cd398a
- Full Text :
- https://doi.org/10.3390/s20174677