An Automatic Detection of Sleep using Different Statistical Parameters of Single Channel EEG Signals

Prakash, A. Praveen; Roy, Vandana

doi:10.14257/ijsip.2016.9.10.32

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Most of the research work is based on the brain EEG signal from the different medical conditioned subjects. Because the EEG signal patterns provide meaningful information directly from the brain which helps to analyze the changes in sleep behavior over the individual sleep stages [10][11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches with Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model

Satapathy¹,

Loganathan²

2022

IJCDS

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Sleep is an essential requirement for human health and well-being, but many people face sleep problems. These problems can lead to several neurological and physical disorders and adversely affect the overall quality of life. Artificial intelligence (AI)based methods for automated sleep stage classification is a fundamental approach to evaluating and treating this public health challenge.The main contribution of this research work is to develop an Automated Sleep Staging System based on Two-Layer Heterogeneous Ensemble Learning Stacking Model (ASSS-TL-HELSM) for sleep staging under the American Academy of Sleep Medicine (AASM) sleep scoring rules. The main aim of this model is to enhance sleep staging accuracy, reduce overfitting and handle overdrift. For signal preprocessing, we use two different feature selection techniques, Fisher Score (FS), and ReliefF (ReF). For feature extraction, we obtain a total of 28 features.The proposed model analyzes the sleep behavior of the subject using the seasonal and trend components. Sleep recordings from two different subgroups of Institute of Systems and Robotics University of Coimbra (ISRUC-Sleep) were obtained for our experiments.Compared with recent studies using single-channel electro encephalogram (EEG) signals, our proposed ASSS-TL-HELSM model shows the best sleep staging classification accuracy performance on a five sleep stages classification (SC-5) task. The overall classification accuracy is 97.93%, and 97% for features selected through FS and ReF respectively, with the subgroup-I(SG-I) data; similarly, for the subgroup-III(SG-III) data, the features selected through FS, and ReF show a classification accuracy of 98.16% and 98.78% respectively. The comparisons between the proposed model and the existing model show that the proposed model gives better sleep staging accuracy for the five-sleep state's classification.

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Section: Introductionmentioning

confidence: 99%