scholarly journals A review of automated sleep stage scoring based on physiological signals for the new millennia

2019 ◽  
Vol 176 ◽  
pp. 81-91 ◽  
Author(s):  
Oliver Faust ◽  
Hajar Razaghi ◽  
Ragab Barika ◽  
Edward J Ciaccio ◽  
U Rajendra Acharya
Keyword(s):  
Sleep Stage ◽  
Physiological Signals ◽  
Sleep Stage Scoring

Combining decision trees classifiers: a case study of automatic sleep stage scoring

2012 ◽  
Vol 2 (1) ◽  
pp. 60 ◽  
Author(s):  
Natheer Khasawneh ◽  
Stefan Conrad ◽  
Luay Fraiwan ◽  
Eyad Taqieddin ◽  
Basheer Khasawneh
Keyword(s):  
Decision Trees ◽  
Sleep Stage ◽  
Sleep Stage Scoring

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

2010 ◽  
Vol 49 (03) ◽  
pp. 230-237 ◽  
Author(s):  
K. Lweesy ◽  
N. Khasawneh ◽  
M. Fraiwan ◽  
H. Wenz ◽  
H. Dickhaus ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discriminant Analysis ◽  
Sleep Stage ◽  
Continuous Wavelet ◽  
Eeg Signal ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Mother Wavelets ◽  
Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

scholarly journals Automated Sleep Stage Scoring Using k-Nearest Neighbors Classifier

2020 ◽  
Vol 5 (53) ◽  
pp. 2377
Author(s):  
Tamás Kiss ◽  
Stephen Morairty ◽  
Michael Schwartz ◽  
Thomas Kilduff ◽  
Derek Buhl ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Sleep Stage Scoring

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

Automatic analysis overcomes limitations of sleep stage scoring

1986 ◽  
Vol 64 (4) ◽  
pp. 364-374 ◽  
Author(s):  
Werner Haustein ◽  
June Pilcher ◽  
Joachim Klink ◽  
Hartmut Schulz
Keyword(s):  
Sleep Stage ◽  
Automatic Analysis ◽  
Sleep Stage Scoring

Sleep-stage scoring in the rat using a support vector machine

2008 ◽  
Vol 168 (2) ◽  
pp. 524-534 ◽  
Author(s):  
Shelly Crisler ◽  
Michael J. Morrissey ◽  
A. Michael Anch ◽  
David W. Barnett
Keyword(s):  
Support Vector Machine ◽  
Sleep Stage ◽  
Support Vector ◽  
Sleep Stage Scoring
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