Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Hsu, Yu-Liang; Yang, Ya-Ting Carolyn; Wang, Jeen-Shing; Hsu, Chia Yang

doi:10.1016/j.neucom.2012.11.003

Cited by 276 publications

(152 citation statements)

References 26 publications

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“…Considering that each selected signal contains 20 epochs of 30 seconds, the algorithm was employed to evaluate a period equivalent to 6 hours and 50 minutes. The signals were collected from F pz channel (EEG electrode positioned at prefrontal region due to its ease of use in a driver facility, as in other studies (Hsu et al, 2013)) and sampled at f s = 100Hz.…”

Section: Resultsmentioning

confidence: 99%

Drowsiness detection for single channel EEG by DWT best m-term approximation

2015

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Introduction: In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods: In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results: By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion: The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.

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Section: Resultsmentioning

confidence: 99%

Drowsiness detection for single channel EEG by DWT best m-term approximation

2015

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“…According to R&K standards, sleep-stages are divided into six stages, namely W (Wakefulness), REM (Rapid Eye Movement), NREM I, II, III, and IV (Non-Rapid Eye Movement). In some studies NREM III and IV stages are combined into a single stage (SWS -Slow-wave Sleep) to increase classification ratio [19,20], but in our study all stages will be separately taken into account in order to show the efficiency of ensemble methods. In the proposed study, sleep-stage classification is performed based on only EEG signals.…”

Section: Methodsmentioning

confidence: 99%

“…The sleep EDF data set includes two channels recorded under the sequential montage. Because the Fpz-Cz channel is more distinctive than others according to some recent research papers [20], we also utilize and focus on Fpz-Cz channel in our study.…”

Section: Methodsmentioning

confidence: 99%

“…Different types of feature selection methods, including the multiple iterative, suitable linear and nonlinear methods have been proposed for classification of sleep stages by Zoubek et al [19], and accuracies of wakefulness (W), NREM I, II, SWS, and REM are obtained as 84.57%, 64.56%, 85.55%, 92.90% and 72.81%, respectively. In [20], the energy features of single-channel EEG signals are utilized for classification of sleep-stages using neural classifiers and EEG epochs were classified as wakefulness, NREM I, II, SWS or REM, and the overall accuracy is 81.8%. In another study, Koley and Dey [21] applied a Support Vector Machine (SVM) based ensemble method on their data set to classify it into five stages as similar to [20] with using different feature extraction methods.…”

Section: Introductionmentioning

confidence: 99%

“…In [20], the energy features of single-channel EEG signals are utilized for classification of sleep-stages using neural classifiers and EEG epochs were classified as wakefulness, NREM I, II, SWS or REM, and the overall accuracy is 81.8%. In another study, Koley and Dey [21] applied a Support Vector Machine (SVM) based ensemble method on their data set to classify it into five stages as similar to [20] with using different feature extraction methods. Furthermore, a type of SVM based recursive feature elimination algorithm is applied on 39 extracted features in order to enhance their result.…”

Section: Introductionmentioning

confidence: 99%

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Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

İlhan

Bilgin

2017

ijisae

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Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and efficient diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on the modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with 'ensemble learning' title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of patient independent computer based diagnosis systems.

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