Inter-rater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm

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.

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Automated Sleep Stage Scoring Using k-Nearest Neighbors Classifier

The Journal of Open Source Software ◽

10.21105/joss.02377 ◽

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

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A hierarchical classification system for sleep stage scoring via forehead EEG signals

2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) ◽

10.1109/ccmb.2013.6609157 ◽

2013 ◽

Cited By ~ 18

Author(s):

Chih-Sheng Huang ◽

Chun-Ling Lin ◽

Li-Wei Ko ◽

Sheng-Yi Liu ◽

Tung-Ping Sua ◽

...

Keyword(s):

Classification System ◽

Sleep Stage ◽

Hierarchical Classification ◽

Eeg Signals ◽

Sleep Stage Scoring

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

Clinical EEG and Neuroscience ◽

10.1177/155005940703800311 ◽

2007 ◽

Vol 38 (3) ◽

pp. 148-154 ◽

Cited By ~ 4

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.

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