Feature selection for sleep/wake stages classification using data driven methods

Zoubek, Lukáš; Charbonnier, Sylvie; Lesecq, Suzanne; Buguet, Alain; Chapotot, Florian

doi:10.1016/j.bspc.2007.05.005

Cited by 132 publications

(88 citation statements)

References 16 publications

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“…To avoid features in greater numeric ranges dominating those in smaller numeric ranges, as well as numerical difficulties during classification; each feature of the transformed matrix is independently normalized to the [0,1] range by applying…”

Section: Feature Transformation and Normalizationmentioning

confidence: 99%

“…Sleep is an active and regulated process with an essential restorative function for physical and mental health [1]. Sleep disorders have an important effect on the health and quality of life.…”

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%

“…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%

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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

119

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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.

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Section: Feature Transformation and Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

119

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show abstract

“…Therefore, many publications can be found in the literature on automat ic sleep/wake stages analysis. Zoubek et al [23] applied frequency and time do main features on sleep EEG. Ferri et al [24] characterized the different levels of EEG synchronization during sleep (in the 0.25-2.5 Hz band) by means of the synchronization likelihood (SL) algorith m and analy zed its long-range temporal correlations.…”

Section: Imentioning

confidence: 99%

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

Goshvarpour

Abbasi²

2013

IJIGSP

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-Scaling behavior is an indicator of the lack of characteristic time scale, and the existence of longrange correlations related to physiological constancy preservation. To investigate the fluctuations of the sleep electroencephalogram (EEG) over various time scales during different sleep stages detrended fluctuation analysis (DFA) is studied. The sleep EEG signals for analysis were obtained fro m the Sleep-EDF Database available online at the PhysioBank. The DFA computations were performed in different sleep stages. The scaling behavior of these time series was investigated with detrended fluctuation analysis (window size: 50 to 500). The results show that the mean values of scaling exponents were lo wer in subjects during stage 4 and standard deviation of scaling exponents of stage 4 was larger than that of the other stages. In contrast, the mean value of scaling exponents of stage 2 was larger, wh ile a small variat ion of scaling exponent is observed at this stage. Therefore, DFA has a more stable behavior in stage 2, whereas the random variability and unpredictable behavior of DFA can be observed in the stage 4. In conclusion, scaling exponent indices are efficacious in quantifying EEG signals in different sleep stages.

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Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

Chapotot

Becq

2009

Adaptive Control & Signal

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International audienceThe classification of sleep-wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep-wake stages using flexible decision rules. An extensive database of 48 PSG records scored in 20s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non-linear candidate features extracted in 2s epochs from EEG and EMG signals, a sequential forward search selected an optimal set of five features with a 22% error rate. Multiple layer perceptrons were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82% accuracy not far from inter-expert expert agreement (83±3%). Significant improvements should be expected using a larger learning set compensating for a high inter-individual variability, and decision rules incorporating more domain-knowledge

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Feature selection for sleep/wake stages classification using data driven methods

Cited by 132 publications

References 16 publications

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

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

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

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