An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Wongsirichot, Thakerng; Elz, Nittida; Kajkamhaeng, Supasit; Nupinit, Wanchai; Sridonthong, Narongrit

doi:10.18178/ijmlc.2019.9.4.835

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Linda Zhang et al and Fernandez-Varela et al [ 5 , 26 ] used a single dataset with three channels (EEG, EMG, and EOG) for five-class classification. Additionally, Wongsirichot et al [ 27 ] used the visit-2 dataset with 14 biomedical channels for 5-class stage classification using a k-nearest neighbor classifier, and achieved an accuracy of 83.76%, which is less than the proposed method. The proposed technique used five channels for three-stage and five-stage classification and employs an ensemble bagged tree classifier, and achieved higher classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

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An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

Sharma

Yadav

Tiwari

et al. 2022

IJERPH

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Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa (κ) coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s κ of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently.

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Section: Discussionmentioning

confidence: 99%

“…Fernández-Varela et al [ 26 ] used only 500 random sleep recordings of the SHHS-1 database and achieved an accuracy of 75% by using a convolutional network. Wongsirichot et al [ 27 ] used the k-nearest neighbors classifier with 14 bio-medical channels of the SHHS-2 database and found an accuracy of 83.76%.…”

Section: Introductionmentioning

confidence: 99%

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

Sharma

Yadav

Tiwari

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The decision tree is commonly used in operations research, specifically in decision analysis to help identify a strategy most likely to reach a goal, but is also a popular tool in machine learning (Sharma & Kaur, 2013;Ariestya et al 2016). In addition, a decision tree is created by recursively selecting the best attributes to split the data and expanding the leaf nodes of the tree until a predefined stopping criterion is met (Wongsirichot et al 2019). Primarily, a decision tree is like a flow chart that classifies instances depending upon the features.…”

Section: Decision Tree -Id3 Classifiermentioning

confidence: 99%

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Menaouer

Zahra

Mohammed

2022

International Journal of Service Science, Management, Engineering, and Technology

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Social media has revolutionized the way people disclose their personal health concerns and express opinions on public health issues. In this paper a new approach for multi-class sentiment classification using supervised learning techniques. The aim of this multi-class sentiment classification is to assign the healthcare Tweets automatically into predetermined categories on the basis of their linguistic characteristics, their contents, and some of the words that characterize each category from the others. Briefly, relevant health datasets are collected from Twitter using Twitter API; then, use of the methodology is illustrated and evaluated against one with only three different algorithms was used, to improve the accuracy of Decision Trees, SMO, and K-NN classifiers. Many experiments accomplished to prove the validity and efficiency of the approach using datasets tweets and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes

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Current status and prospects of automatic sleep stages scoring: Review

et al. 2023

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The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.

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An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Cited by 5 publications

References 6 publications

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

Multi-Class Sentiment Classification for Healthcare Tweets Using Supervised Learning Techniques

Current status and prospects of automatic sleep stages scoring: Review

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