Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Braun, Eduardo Tiago; Kozakevicius, Alice de Jesus; Silveira, Thiago L. T. da; Rodrigues, Cesar Ramos; Baratto, Giovani

doi:10.22456/2175-2745.74030

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…Eduardo T. Braun et al [27] developed a portable sleep staging system using a different combination of features from EEG signals. The proposed method presented the best classification accuracy of 97.1 % for the two-state classification.…”

Section: B Significancementioning

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: B Significancementioning

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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“…Eduardo T. Braun presented an efficient and effective sleep staging scoring system and obtained frequency domain properties. They obtained random forest (RF) techniques and the achieved results for a classification model as 90.9%, 91.8%, 92.4%, 94.3%, and 97.1% for 2–6 sleep stages respectively [36].…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of machine learning algorithms on automated sleep staging feature sets

Satapathy

Loganathan

Kondaveeti

et al. 2021

CAAI Trans on Intel Tech

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With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep-related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods. For the purpose of accurate diagnosis of different sleep disorders, we have considered the automated analysis of sleep epochs, which were collected from the subjects during sleep time. The complete process of an automated approach of sleep stages' classification is majorly executed through four steps: pre-processing the raw signals, feature extraction, feature selection, and classification. In this study, we have extracted 12 statistical properties from input signals. The proposed models are tested in three different combinations of features sets. In the first experiment, the feature set contained all the 12 features. The second and third experiments were conducted with the nine and five best features. The patient records come from the ISRUC-Sleep database. The highest classification accuracy was achieved for sleep staging through combinations with the five feature set. From the categories of the subjects, the reported accuracy results were found to exceed above 90%. As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Fraiwan et al [51] extracted time-frequency entropy features to represent the sleep records and used a linear discriminate analysis algorithm for classifying the sleep stages and the overall classification accuracy achieved for six-state sleep stages was 84%.…”

Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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Sleep staging is one of the important methods for the diagnosis of the different types of sleep-related diseases. Manual inspection of sleep scoring is a very time-consuming process, labor-intensive, and requires more human interpretations, which may produce biased results. Therefore, in this paper, we propose an efficient automated sleep staging system to improve sleep staging accuracy. In this work, we extracted both linear and non-linear properties from the input signal. Next to that, a set of optimal features was selected from the extracted feature vector by using a feature reduction technique based on the ReliefF weight algorithm. Finally, the selected features were classified through four machine learning techniques like support vector machine, K-nearest neighbor, decision tree, and random forest. The proposed methodology performed using dualchannel EEG signals from the ISRUC-Sleep dataset under the AASM sleep scoring rules. The performance of the proposed methodology compared with the existing similar methods. In this work, we considered the 10-Fold cross validation strategy; our proposed methods reported the highest classification accuracy of 91.67% with the C4-A1 channel, and 93.8% with the O2-A1 channel using the Random forest classification model. The result of the proposed methodology outperformed the earlier contribution for two-class sleep states classification. The proposed dual-channel sleep staging method can be helpful for the clinicians during the sleep scoring and treatment for the different sleep-related diseases.

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Sleep Stages Classification Using Spectral Based Statistical Moments as Features

Cited by 16 publications

References 32 publications

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

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

Performance analysis of machine learning algorithms on automated sleep staging feature sets

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

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