Sleep stage classification using single-channel EOG

Rahman, Mizanur; Bhuiyan, Mohammed Imamul Hassan; Hassan, Ahnaf Rashik

doi:10.1016/j.compbiomed.2018.08.022

Cited by 142 publications

(72 citation statements)

References 24 publications

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“…Geng et al used the multi-scale entropy feature combined with SVM-RF classifiers to classify and evaluate different pain types with an accuracy of more than 93% [ 18 ]. Rahman et al [ 47 ] used statistical features, such as spectral entropy and refined composite multiscale dispersion entropy (RCMDE) in the discrete wavelet transform (DWT) domain analysis of single-channel EOG signals and used RUSBoost classifier for automatic sleep stage classification with an average accuracy of 84.70%. Liang et al [ 48 ] used the multiscale entropy method to process EEG signals and linear discriminant analysis to conduct automatic sleep staging with an average accuracy of 76.91%.…”

Section: Introductionmentioning

confidence: 99%

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

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The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

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Section: Introductionmentioning

confidence: 99%

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

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“…Using MSE, an increased EEG signal complexity was found in Parkinson's disease (PD) patients during non-rapid eye movement sleep at high scale factors [21]. MDE was successfully used for sleep stage classification using single-channel electrooculography signals [22]. Miskovic et al showed that slow sleep EEG data were characterized by reduced MDE values at low scales and increased MDE values at high scale factors [23].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

et al. 2019

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Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this article. MFDE is robust to the presence of baseline wanders, or trends, in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. MFDE dealt with the problem of undefined MSE values and, compared with MDE, led to more stable entropy values over the scale factors for pink noise. Overall, MFDE was the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along the signal (e.g., eye movement data). This study shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases recordings.

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“…In our experiment, we have used the ISRUC-Sleep database which is more balanced Notice that with a relatively balanced database, SVM and RF still performs poorly to classify the S1 sleep stage. However, RUSBoost consistently produces a satisfactory performance [9].…”

Section: Introductionmentioning

confidence: 95%

An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

Wongsirichot¹,

Elz²,

Kajkamhaeng³

et al. 2019

IJMLC

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Sleep quality is highly significant for the people's overall health. A standard diagnosis for sleep-related syndromes and illnesses is Polysomnography (PSG) or a sleep test in a controlled laboratory. However, PSG requires a sleep specialist to interpret bio-signals collected. It is a time consuming procedure. One of the fundamental step in the PSG is Sleep Stage Classification (SSC). In this study, we propose an investigation of Automatic Sleep Stage Classification (ASSC) using data mining techniques as an alternative to the PSG in order to reduce the time necessary for accurately diagnosing sleep quality. We studied 2,535 subjects' polysomnographic data with 14 channels of biomedical signals from the Sleep Heart Health Study (SHHS) Dataset. Subsequently, four data mining techniques including Decision Trees, Random Forests, Neural Network, and k-Nearest Neighbors were selected to compare the classification performances. The classification results in k-Nearest Neighbors achieved the greatest accuracy at 83.76%.

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Sleep stage classification using single-channel EOG

Cited by 142 publications

References 24 publications

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

An Investigation of Data Mining Based Automatic Sleep Stage Classification Techniques

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