A novel technique for stress detection from EEG signal using hybrid deep learning model

Malviya, Lokesh; Mal, Sandip

doi:10.1007/s00521-022-07540-7

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…The AlexNet architecture was utilized for classification, distinguishing between calm and distressed emotional states with an accuracy of 84%. The authors of 26 conducted stress detection using EEGMAT data, applying the Discrete Wavelet Transform (DWT) to 19 out of 23 EEG channels. The classification, facilitated by a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BLSTM) architecture, successfully differentiated between stressed and relaxed states, achieving an impressive accuracy of 99.2%.…”

Section: Related Workmentioning

confidence: 99%

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Amer,

Belhaouari

2024

Sci Rep

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Brain disorders pose a substantial global health challenge, persisting as a leading cause of mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain disorders, but it can be challenging for medical practitioners to interpret complex EEG signals and make accurate diagnoses. To address this, our study focuses on visualizing complex EEG signals in a format easily understandable by medical professionals and deep learning algorithms. We propose a novel time–frequency (TF) transform called the Forward–Backward Fourier transform (FBFT) and utilize convolutional neural networks (CNNs) to extract meaningful features from TF images and classify brain disorders. We introduce the concept of eye-naked classification, which integrates domain-specific knowledge and clinical expertise into the classification process. Our study demonstrates the effectiveness of the FBFT method, achieving impressive accuracies across multiple brain disorders using CNN-based classification. Specifically, we achieve accuracies of 99.82% for epilepsy, 95.91% for Alzheimer’s disease (AD), 85.1% for murmur, and 100% for mental stress using CNN-based classification. Furthermore, in the context of naked-eye classification, we achieve accuracies of 78.6%, 71.9%, 82.7%, and 91.0% for epilepsy, AD, murmur, and mental stress, respectively. Additionally, we incorporate a mean correlation coefficient (mCC) based channel selection method to enhance the accuracy of our classification further. By combining these innovative approaches, our study enhances the visualization of EEG signals, providing medical professionals with a deeper understanding of TF medical images. This research has the potential to bridge the gap between image classification and visual medical interpretation, leading to better disease detection and improved patient care in the field of neuroscience.

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Section: Related Workmentioning

confidence: 99%

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Amer,

Belhaouari

2024

Sci Rep

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“…The BiLSTM learning technique is a series of processing models that includes two LSTM networks: the first one acts in a forward direction, and second one in a backwards direction [68,69]. BiLSTMs effectively increase the amount of information available to the network, giving the algorithm more context.…”

Section: Bidirectional Lstmmentioning

confidence: 99%

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals

et al. 2023

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Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.

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“…In neuroscience and psychology research, EEG is extensively employed as a powerful tool for investigating the brain and its functionalities. Applications like cognitive and affective monitoring show great promise, offering the potential for unbiased measurements of various aspects, such as an individual's fatigue level, mental workload, mood, or psychological states like stress [1]. Electroencephalography (EEG) signals represent the measurement of electric fields produced by the active brain.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Recurrent Neural Network Model for Stress Detection in EEG Signals

Mhaouch,

Fradi,

Gtifa

et al. 2024

RIA

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A novel technique for stress detection from EEG signal using hybrid deep learning model

Cited by 13 publications

References 33 publications

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Exploring new horizons in neuroscience disease detection through innovative visual signal analysis

Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals

Deep Learning Based Recurrent Neural Network Model for Stress Detection in EEG Signals

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