Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Hasan, Junayed; Shon, Dongkoo; Im, Ki Chun; Choi, Heon‐Jin; Yoo, Dae-Seung; Kim, Jong‐Myon

doi:10.3390/app10217639

Cited by 34 publications

(13 citation statements)

References 48 publications

(77 reference statements)

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“…Simple CNN, ResNet, WaveNet, and Inception are among the best CNNs networks widely used in biomedical signals analysis studies. Based on recent works [ 42 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], a comparative analysis is provided in the following using various performance criteria as complexity , 1D-dimension , performance and time-consumption . In this regard, specific three tests (2, 3 and 4 states) with various values are given for each criterion as following.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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show abstract

“…[31]). Recently, using psychological signals to investigate Alzheimer, mental and sleep disorders becomes very common [32][33][34][35][36][37][38][39]. Moreover, the EEG has become a crucial non-invasive measure of brain activities, and it has a vital potentiality to diagnose mental disorders, abnormalities, and the state of the brain [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

AlShorman¹,

Masadeh²,

Heyat³

et al. 2022

J. Integr. Neurosci.

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Stress has become a dangerous health problem in our life, especially in student education journey. Accordingly, previous methods have been conducted to detect mental stress based on biological and biochemical e fects. Moreover, hormones, physiological e fects, and skin temperature have been extensively used for stress detection. However, based on the recent literature, biological, biochemical, and physiological-based methods have shown inconsistent findings, which are initiated due to hormones' instability. Therefore, it is crucial to study stress using di ferent mechanisms such as Electroencephalogram (EEG) signals. In this research study, the frontal lobes EEG spectrum analysis is applied to detect mental stress. Initially, we apply a Fast Fourier Transform (FFT) as a feature extraction stage to measure all bands' power density for the frontal lobe. A ter that, we used two type of classifications such as subject wise and mix (mental stress vs. control) using Support Vector Machine (SVM) and Naive Bayes (NB) machine learning classifiers. Our obtained results of the average subject wise classification showed that the proposed technique has better accuracy (98.21%). Moreover, this technique has low complexity, high accuracy, simple and easy to use, no over fitting, and it could be used as a real-time and continuous monitoring technique for medical applications.

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“…The power spectral density is a powerful tool that applies Fourier transforms to analyse the amount of power of a signal for determined frequencies, and it can be estimated through different techniques [48][49][50]. In the context of EEG and biomedical systems, it has been applied in several different situations including to analyse the effects of age and gender [51], disruptions caused by Alzheimer's [52], cognitive alterations when patients are under mental stress [53], and sleep classification [54].…”

Section: Introductionmentioning

confidence: 99%

Fractal Methods and Power Spectral Density as Means to Explore EEG Patterns in Patients Undertaking Mental Tasks

2021

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Brain electrical activity recorded as electroencephalogram data provides relevant information that can contribute to a better understanding of pathologies and human behaviour. This study explores extant electroencephalogram (EEG) signals in search of patterns that could differentiate subjects undertaking mental tasks and reveals insights on said data. We estimated the power spectral density of the signals and found that the subjects showed stronger gamma brain waves during activity while presenting alpha waves at rest. We also found that subjects who performed better in those tasks seemed to present less power density in high-frequency ranges, which could imply decreased brain activity during tasks. In a time-domain analysis, we used Hall–Wood and Robust–Genton estimators along with the Hurst exponent by means of a detrented fluctuation analysis and found that the first two fractal measures are capable of better differentiating signals between the rest and activity datasets. The statistical results indicated that the brain region corresponding to Fp channels might be more suitable for analysing EEG data from patients conducting arithmetic tasks. In summary, both frequency- and time-based methods employed in the study provided useful insights and should be preferably used together in EEG analysis.

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Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Cited by 34 publications

References 48 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection

Fractal Methods and Power Spectral Density as Means to Explore EEG Patterns in Patients Undertaking Mental Tasks

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