A tool for analysis and classification of sleep stages

Le, Quoc Khai; Truong, Quang Dang Khoa; Vo, Toi Van

doi:10.1109/atc.2011.6027492

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…In smoothing, temporal contextual information and sleep transitions are considered. Temporal Contextual Information Smoothing: in smoothing, we implement generally accepted smoothing rules: the ''3-minutes rule'' [38].…”

Section: Hybrid Expert System: the New Paradigmmentioning

confidence: 99%

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Chen

Ugon²,

Sun

et al. 2019

IEEE Access

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Identification of sleep stages is a fundamental step in clinical sleep analysis. Existing automatic sleep staging systems ignore two major issues: 1) Most of existing automatic sleep staging systems are using numerical classification methods without involving medical knowledge. These kinds of systems are not yet understood and accepted by physicians. 2) Individual variability sources are ignored. However, individual variability is observed in many aspects of sleep research (such as polysomnography recordings, sleep patterns, and sleep architecture). In this paper, a hybrid expert system is proposed to mimic the decisionmaking process of clinical sleep staging in accordance with the medical knowledge by using symbolic fusion. To formalize the medical guideline and knowledge, thresholds are used for translating the sleep events into symbols and the sleep event's threshold dependencies are analyzed for fully understanding the thresholds dependencies among different sleep stages and subjects. Meanwhile, the differential evolution algorithm is adopted to automate the setting-up of thresholds that are used in the symbolic fusion model and to provide personalized thresholds, which allows taking the individual variability into consideration. The robustness and clinical applicability of the proposed system are evaluated and demonstrated on a clinical dataset. The dataset is composed of 16 patients (nine males and seven females) and scored by physicians. Only 5% of the dataset is used for the training process to obtain the personalized thresholds. Then, these personalized thresholds are passed to the classification process, and the overall accuracy on the identification of five sleep stages reaches 80.09%. Using a small dataset for the training process, the proposed system not only drastically reduces the training set but also achieves favorable results compared with most of the existing works.

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Section: Hybrid Expert System: the New Paradigmmentioning

confidence: 99%

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Chen

Ugon²,

Sun

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection

Sriraam

Shri

Maheshwari

2016

Australas Phys Eng Sci Med

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Electroencephalographic (EEG) activity recorded during the entire sleep cycle reflects various complex processes associated with brain and exhibits a high degree of irregularity through various stages of sleep. The identification of transition from wakefulness to stage1 sleep is a challenging area of research for the biomedical community. In this paper, spectral entropy (SE) is used as a complexity measure to quantify irregularities in awake and stage1 sleep of 8-channel sleep EEG data from the polysomnographic recordings of ten healthy subjects. The SE measures of awake and stage1 sleep EEG data are estimated for each second and applied to a multilayer perceptron feed forward neural network (MLP-FF). The network is trained using back propagation algorithm for recognizing these two patterns. Initially, the MLP network is trained and tested for randomly chosen subject-wise combined datasets I and II and then for the combined large dataset III. In all cases, 60 % of the entire dataset is used for training while 20 % is used for testing and 20 % for validation. Results indicate that the MLP neural network learns with maximum testing accuracy of 95.9 % for dataset II. In the case of combined large dataset, the network performs with a maximum accuracy of 99.2 % with 100 hidden neurons. Results show that in channels O1, O2, F3 and F4 (A1, A2 as reference), the mean of the spectral entropy value is higher in awake state than in stage1 sleep indicating that the EEG becomes more regular and rhythmic as the subject attains stage1 sleep from wakefulness. However, in C3 and C4 the mean values of SE values are not very much discriminative of both groups. This may prove to be a very effective indicator for scoring the first two stages of sleep EEG and may be used to detect the transition from wakefulness to stage1 sleep.

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A simple laboratory test of music therapy for insufficient sleep

Phasukkit

Mahrozeh

Kumngern

et al. 2015

2015 8th Biomedical Engineering International Conference (BMEiCON)

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This article studies the behavior of human sleep found the insufficient sleep problem due to the brain performance. This experiment uses devices such as sleeptracker to save the results of a sleep and electroencephalogram (EEG) to detect brainwaves while enjoying music. However, this study considers only a general sleep and sleep event that a relaxed with the music. The findings indicated the music can help to reduce the problems of insufficient sleep. As a result, volunteers are developing the sleep as sufficiently and efficiently due to get the best quality of sleep in the life. It is also available to deploy to the general person in order to solve the problem of insufficient sleep.

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A tool for analysis and classification of sleep stages

Cited by 5 publications

References 10 publications

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Towards a Hybrid Expert System Based on Sleep Event’s Threshold Dependencies for Automated Personalized Sleep Staging by Combining Symbolic Fusion and Differential Evolution Algorithm

Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection

A simple laboratory test of music therapy for insufficient sleep

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