Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier

Fraiwan, Luay; Lweesy, Khaldon; Khasawneh, Natheer; Wenz, Heinrich; Dickhaus, Hartmut

doi:10.1016/j.cmpb.2011.11.005

Cited by 385 publications

(235 citation statements)

References 29 publications

Supporting

Mentioning

219

Contrasting

Unclassified

Order By: Relevance

“…The low recall and precision values for these stages could be due to intra-subjects variability in the EEG signals, as described in [47,48]. Furthermore, these results are consistent with previous reports that suggest a significant EEG similarity between these stages [8], reflected in low discrimination values for both automatic classification [42,49] or inter-scorer agreement [11].…”

Section: Featuresupporting

confidence: 82%

“…These results support the studies that propose the use of unsupervised clustering techniques in order to address the sleep stage classification problem [35]. The performance archived by this sleep stage detection algorithm is similar (or superior) to previous results, for both supervised [23,36] and unsupervised applications [35]. Clustering methods do not depend on a previous training process, and therefore tend to be less influenced by signal differences among subjects or specific conditions [37].…”

Section: Featuresupporting

confidence: 72%

“…The changes in sleep stages cause changes in the EEG, and these changes are also expected to be reflected in the entropy results, as observed in many other works, see [8] (pp. 193-208, Chapter 10) and [18,20,23]. The specific features computed in this study using the standard algorithms were: Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA), Shannon entropy (H), Approximate Entropy (ApEn), Sample Entropy (SampEn), and Multiscale Entropy (MSE).…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

107

View full text Add to dashboard Cite

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

show abstract

Section: Featuresupporting

confidence: 82%

Section: Featuresupporting

confidence: 72%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

107

View full text Add to dashboard Cite

show abstract

“…Some works such as [8,13] used just one or more EEG channels, whereas others [1,7,14] used EEG channels in combination with EOG and EMG channels. Therefore, to reduce the computational cost and improve classification performance, a systematic analysis for finding the best combination of EEG, EOG and EMG channels, for both application sleep-wake detection and multiclass sleep staging, is performed.…”

Section: Introductionmentioning

confidence: 99%

“…It is also a somewhat subjective procedure in which the concordance between the results of visual scoring obtained by experts can vary greatly. Accordingly, [1,[6][7][8]. Different parametric and nonparametric methods have been applied in the classification process such as random forest classifiers, artificial neural networks (ANN), fuzzy logic, the nearest neighbour, linear discriminant analysis (LDA,) support vector machine (SVM) and kernel logistic regression (KLR) [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Khalighi

Sousa

Pires

et al. 2013

Expert Systems with Applications

122

View full text Add to dashboard Cite

To improve applicability of automatic sleep staging an efficient subjectindependent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and timefrequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.

show abstract

Polysomnography and Sleep Analysis

2020

Body Sensor Networking, Design and Algorithms

View full text Add to dashboard Cite

Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier

Cited by 385 publications

References 29 publications

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

Polysomnography and Sleep Analysis

Contact Info

Product

Resources

About