Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm

Obayya, Marwa; Abou-Chadi, Fatma E. Z.

doi:10.1109/nrsc.2014.6835085

Cited by 31 publications

(10 citation statements)

References 13 publications

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“…In some ASSC systems, feature selection and/or dimensionality reduction is performed prior to the classification stage to reduce the number of features, or generate new low-dimensional features that are derived from the input features. Examples can be found in [9,41,51,60]. Finally, the extracted attributes are passed to one or more classifiers to categorize human sleep stages.…”

Section: Electroencephalogrammentioning

confidence: 99%

“…Kayikcioglu et al [49] extracted Auto-Regressive (AR) coefficient features from a single EEG signal to classify both sleep and wake stages with an accuracy of 91% using a Partial Least Squares Regression (PLSR) classifier. Spectral analysis, Wavelet Transform (WT) and fuzzy clustering based on the FCM algorithm were used by [60] in an automatic sleep stage detector, which was able to distinguish the wake stage, as well as stages 1-4 and REM sleep stage, using single-channel EEG signals. The results showed that the algorithm could provide a 92.27% success rate when using wavelet packets.…”

Section: State Of the Artmentioning

confidence: 99%

“…Principal components are assured to be autonomous only if the dataset is equally and normally distributed. PCA is also called discrete Karhunen-Loeve Transform (KLT) [60,138]. KLT is mostly used in statistical pattern recognition where the main function is to find the representation of the input vector in terms of the eigenvectors of their covariance matrix.…”

Section: Principal Component Analysismentioning

confidence: 99%

See 2 more Smart Citations

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

291

124

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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Section: Electroencephalogrammentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

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Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

Entropy

291

124

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“…The accuracy results of six-state classification are able to attain rates 87.5%. Spectral analysis, Wavelet transform and Fuzzy clustering based on Fuzzy C-Means algorithm (FCM) were used by [13] for an automatic sleep stage detector, which can separate Awake stage, Stage1, Stage2, Stage3, Stage4 and REM stage using single channel EEG signals. The results showed that the algorithm could provide 92.27% success rate when using wavelet packets.…”

Section: B Related Workmentioning

confidence: 99%

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

Aboalayon

Almuhammadi

Faezipour

2015

2015 Long Island Systems, Applications and Technology

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In recent years, the estimation of human sleep disorders from Electroencephalogram (EEG) signals have played an important role in developing automatic detection of sleep stages. A few methods exist in the market presently towards this aim. However, sleep physicians may not have full assurance and consideration in such methods due to concerns associated with systems accuracy, sensitivity and specificity. This paper presents a novel and efficient technique that can be implemented in a microcontroller device to identify sleep stages in an effort to assist physicians in the diagnosis and treatment of related sleep disorders by enhancing the accuracy of the developed algorithm using a single channel of EEG signals. First, the dataset of EEG signal is filtered and decomposed into delta, theta, alpha, beta and gamma subbands using Butterworth band-pass filters. Second, a set of sample statistical discriminating features are derived from each frequency band. Finally, sleep stages consisting of Wakefulness, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM) are classified using several supervised machine learning classifiers including multi-class Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), K-Nearest Neighbors (KNN) and Naive Bayes (NB). This paper combines REM with Stage 1 NREM due to data similarities. Performance is then compared based on single channel EEG signals that were obtained from 20 healthy subjects. The results show that the proposed technique using DT classifier efficiently achieves high accuracy of 97.30% in differentiating sleeps stages. Also, a comparison of our method with some recent available works in the literature reiterates the high classification accuracy performance.

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“…The proposed method was found efficient for single channel sleep classification and obtained 87.50% accuracy. For automatic classification of sleep EEG data, Obayya et al[23] deployed wavelet transform and fuzzy clustering algorithm (FA).…”

mentioning

confidence: 99%

Human Sleep Scoring Based on K-Nearest Neighbors

Qureshi¹,

Karrila²,

Vanichayobon³

2018

Turk J Elec Eng & Comp Sci

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Human sleep is one of the essential indicators that gauge the overall health and well-being. Presently, it is common for people to face issues related to sleep. Various biomedical signals including electroencephalogram (EEG), electrooculography (EMG), and electrooculography (EOG) are utilized in the diagnosis and during the treatment of sleep disorder cases. An automatic classification to diagnose sleep problems can help in the analysis of sleep EEG data. In this current study, an effort is made to classify the sleep stages from a single EEG channel (C4-A1) based on K-nearest neighbors (K-NN) with three alternative distance metrics. The Euclidean distance is the most commonly used distance measure in K-NN, and no prior study of sleep EEG data has inspected the classification performance of K-NN with various distance measures. Therefore, this study aimed to investigate whether the distance function affects the performance of K-NN in the classification of sleep data. Euclidean, Manhattan and Chebyshev distance measures were individually tested with K-NN classification, and their performances were compared based on accuracy, sensitivity, specificity, F-measure, Kappa statistic and computation time for both Rechtschaffen & Kales and American Academy of Sleep Medicine standard labelings of the sleep stages. The experimental results show that the Manhattan distance function with K = 5 was the best choice for classification of the sleep stages, achieving 98.46% and 98.77% correct rates for the two labelings with comparatively rapid computations.

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Automatic classification of sleep stages using EEG records based on Fuzzy c-means (FCM) algorithm

Cited by 31 publications

References 13 publications

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

Human Sleep Scoring Based on K-Nearest Neighbors

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