Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting

Güneş, Salih; Polat, Kemal; Yosunkaya, Şebnem

doi:10.1016/j.eswa.2010.04.043

Cited by 195 publications

(75 citation statements)

References 20 publications

(27 reference statements)

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“…On our database, overall 76% AR is achieved for symbolic fusion, which is better in comparison to other decision support algorithms, as reported in [10,18,19]. It proved to be an effective method in comparison to decision tree method, which has AR up to 70% [10].…”

Section: Resultsmentioning

confidence: 53%

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Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

Chen¹,

Xue²,

Ugon³

et al. 2015

EAI Endorsed Trans Perv Health Tech

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With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm-Symbolic Fusion for sleep staging application. The proposed algorithm provides high accuracy by combining data from heterogeneous sources, like EEG, EOG and EMG. This algorithm is developed for implementation in portable embedded systems for automatic sleep staging at low complexity and cost. The proposed algorithm proved to be an efficient design support method and achieved up to 76% overall agreement rate on our database of 12 patients.

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Section: Resultsmentioning

confidence: 53%

“…In [18], authors reported 61% AR by using Linear Discriminant Analysis. In [19], authors reported AR of 55.88%, by using K-Nearest Neighbor method. Proposed method shows the same AR as in [12].…”

Section: Resultsmentioning

confidence: 99%

Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

Chen¹,

Xue²,

Ugon³

et al. 2015

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…The C4.5 decision tree learning is a method used for discrete-valued functions classifying, in which a C4.5 decision tree depicts the learned function [39]. The objective of C4.5 decision tree learning is to partition the recursive data into subgroups (see [39] for more information on C4.5 decision tree learning).…”

Section: Decision Trees (C45)mentioning

confidence: 99%

“…The objective of C4.5 decision tree learning is to partition the recursive data into subgroups (see [39] for more information on C4.5 decision tree learning).…”

Section: Decision Trees (C45)mentioning

confidence: 99%

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Şen¹,

Peker²

2013

Turk J Elec Eng & Comp Sci

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This paper presents a new application for automated epileptic detection using the fast correlation-based feature (FCBF) selection and classification algorithms. This study consists of 3 stages: feature extraction, feature selection from electroencephalography (EEG) signals, and the classification of these signals. In the feature extraction phase, 16 attribute algorithms are used in 5 categories, and 36 feature parameters are obtained from these algorithms.In the feature selection phase, the FCBF algorithm is chosen to select a set of attributes that best represent the EEG signals. The resulting attributes are used as input parameters for the classification algorithms. In the classification phase, the problem is classified with 6 different classification algorithms. The results obtained with the different classification algorithms are provided in order to compare the calculation times and the accuracy rates. The evolution of the proposed system is conducted using k-fold cross-validation, classification accuracy, sensitivity and specificity values, and a confusion matrix. The proposed approach enables 100% classification accuracy with the use of the multilayer perceptron neural network and naive Bayes algorithm. The stated results show that the proposed method is capable of designing a new intelligent assistance diagnostic system.

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“…In response to these challenges, many automatic sleep staging methods using statistical learning or artificial intelligence techniques have been developed (Park et al, 2000, Agarwal and Gotman, 2001, Caffarel et al, 2006, Porée et al, 2006, Tagluk et al, 2010and Pan et al, 2012. The complexity of the sleep staging problems can be furthered alleviated by extracting features from fewer signal channels (Agarwal et al, 2005, Berthomier et al, 2007, Virkkala et al, 2007a, Virkkala et al, 2007b, Malinowska et al, 2009, Güneşa et al, 2010, Levendowski et al, 2012and Stepnowsky et al, 2013.…”

Section: Introductionmentioning

confidence: 99%

Detecting slow wave sleep using a single EEG signal channel

Luo

Hong

et al. 2015

Journal of Neuroscience Methods

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Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods.

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Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting

Cited by 195 publications

References 20 publications

Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

Novel approaches for automated epileptic diagnosis using FCBF selection and classification algorithms

Detecting slow wave sleep using a single EEG signal channel

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