Sleep Stages Classification using Artificial Neural Network

Venkatesh, Kurapati; Geetha, S.

doi:10.17485/ijst/2015/v8i31/87271

Cited by 2 publications

(1 citation statement)

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“…The proposed algorithm was applied for the adaptive recognition and classification of sleep stages [20,[28][29][30][31][32] using both EEG data [13,33] and PSG signals, using their spectral components for the estimation of sleep features. Sleep scoring assumes the knowledge of the Flow, EMG, and EOG records in addition to EEG signals but they affect EEG records as well.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

et al. 2018

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Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man-machine interaction.

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