A Sleep Monitoring Method with EEG Signals

Abichou, Yessrine; Chaabene, Siwar; Chaâri, Lotfi

doi:10.1007/978-3-030-49815-3_4

Cited by 4 publications

(6 citation statements)

References 8 publications

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“…Based on multiple experimental settings, the best configuration was obtained with a dropout of 0.85 for the convolutional part and 0.5 before the final classification. The obtained accuracy rate is higher that the one reported in [10] (93%) where the authors showed that their approach outperforms other state-of-the-art methods.…”

Section: A Validation With a Frame Leave-p-out Strategycontrasting

confidence: 63%

“…Due to the difficulty to define a relevant analytical model for the drowsy state, machine learning tools have been investigated in the literature. In this sense, support vector machines (SVM) have been proposed to analyse EEG signals [9,10]. With the emergence of deep CNN, many studies have been conducted by proposing different architectures for EEG analysis [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

Geoffroy

Chaâri

Tourneret

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Drowsiness detection is still an open issue, especially when detection is based on physiological signals. In this sense, light non-invasive modalities such as electroencephalography (EEG) are usually considered. EEG data provides informations about the physiological brain state, directly linked to the drowsy state. Electrocardigrams (ECG) signals can also be considered to involve informations related to the heart state. In this study, we propose a method for drowsiness detection using joint EEG and ECG data. The proposed method is based on a deep learning architecture involving convolutional neural networks (CNN) and recurrent neural networks (RNN). High efficiency level is obtained with accuracy scores up to 97% on validation set. We also demonstrate that a modification of the proposed architecture by adding autoencoders helps to compensate the performance drop when analysing subjects whose data is not presented during the learning step.

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Section: A Validation With a Frame Leave-p-out Strategycontrasting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

Geoffroy

Chaâri

Tourneret

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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“…Over this decade, many EEG-based research works related to machine learning (ML) [ 27 , 28 , 29 , 30 ] have been suggested in medical diagnosis, in particular for classification-based drowsiness detection tasks. Nevertheless, some limitations appear in ML applications such as the need for a massive dataset to train, limitation predictions in return, the need of an intermediary step for feature representation and drawing conclusions to detect anomalies.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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“…Drowsiness is a major factor in high rates of vehicle accidents. The use of non-invasive biomedical signals for drowsiness detection is an important and open issue that has recently received a considerable interest [54]- [58].…”

Section: Real-world Polysomnographic Data Analysismentioning

confidence: 99%

“…The obtained classification and detection performance are quantified using 2-class accuracies as in [58], and F-measure and area-under-curve (AUC) for the receiver operational characteristics (ROCs), as reported in Table III. To further illustrate the detection performance, the ROCs are displayed in Fig.…”

Section: B Drowsiness Detectionmentioning

confidence: 99%

A Bayesian Framework for Multivariate Multifractal Analysis

Leon

Wendt

Tourneret

et al. 2022

IEEE Trans. Signal Process.

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Multifractal analysis has become a reference tool for signal and image processing. Grounded in the quantification of local regularity fluctuations, it has proven useful in an increasing range of applications, yet so far involving only univariate data (scalar valued time series or single channel images). Recently the theoretical ground for multivariate multifractal analysis has been devised, showing potential for quantifying transient higher-order dependence beyond linear correlation among collections of data. However, the accurate estimation of the parameters associated with a multivariate multifractal model remains challenging, especially for small sample size data. This work studies an original Bayesian framework for multivariate multifractal estimation, combining a novel and generic multivariate statistical model, a Whittle-based likelihood approximation and a data augmentation strategy allowing parameter separability. This careful design enables efficient estimation procedures to be constructed for two relevant choices of priors using a Gibbs sampling strategy. Monte Carlo simulations, conducted on synthetic multivariate signals and images with various sample sizes and multifractal parameter settings, demonstrate significant performance improvements over the state of the art, at only moderately larger computational cost. Moreover, we show the relevance of the proposed framework for real-world data modeling in the important application of drowsiness detection from multichannel physiological signals.

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A Sleep Monitoring Method with EEG Signals

Cited by 4 publications

References 8 publications

Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

A Bayesian Framework for Multivariate Multifractal Analysis

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