Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals

Afzali, Ahmad; Khaleghi, Ali; Hatef, Boshra; Movahed, Reza Akbari; Jahromi, Gila Pirzad

doi:10.1080/17455030.2023.2187237

Cited by 16 publications

(7 citation statements)

References 33 publications

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“…Similar researches have investigated the application of different deep learning models in EEG for epilepsy diagnosis [49, 50], psychiatric disorder diagnosis [20,51], motion imagery classification [52,53] and mental workload classification [54]. In Table IV, a comparison is made between the proposed approach and other leading ML methods for diagnosing ADHD using automated EEG data on the same dataset.…”

Section: Discussionmentioning

confidence: 99%

“…classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG," Journal of Neural Engineering, vol. 20…”

Section: Discussionmentioning

confidence: 99%

“…Extensive research has been conducted on various aspects of EEG signals in individuals with ADHD, including power spectrum density, event-related potentials, multivariate and www.ijacsa.thesai.org univariate EEGs, complexity analysis, and alpha asymmetry [18,19]. While machine learning (ML) algorithms like logistic regression, LDA, SVM, KNN, principle component analysis, and various neural network models have commonly been used to classify EEG patterns in ADHD [20], deep learning models in this field have received relatively less attention and require further investigation. Some studies have focused on applying convolutional neural networks (CNNs) to detect ADHD using functional and structural MRI [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Attention-based Deep Learning Architectures for Classifying EEG in ADHD and Typical Children

Han,

Jin,

2024

IJACSA

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Although limited research has explored the integration of electroencephalography (EEG) and deep learning approaches for attention deficit hyperactivity disorder (ADHD) detection, using deep learning models for actual data, including EEGs, remains a difficult endeavour. The purpose of this work was to evaluate how different attention processes affected the performance of well-established deep-learning models for the identification of ADHD. Two specific architectures, namely long short-term memory (LSTM)+ attention (Att) and convolutional neural network (CNN)s+Att, were compared. The CNN+Att model consists of a dropout, an LSTM layer, a dense layer, and a CNN layer merged with the convolutional block attention module (CBAM) structure. On top of the first LSTM layer, an extra LSTM layer, including T LSTM cells, was added for the LSTM+Att model. The information from this stacked LSTM structure was then passed to a dense layer, which, in turn, was connected to the classification layer, which comprised two neurons. Experimental results showed that the best classification result was achieved using the LSTM+Att model with 98.91% accuracy, 99.87% accuracy, 97.79% specificity and 98.87% F1score. After that, the LSTM, CNN+Att, and CNN models succeeded in classifying ADHD and Normal EEG signals with 98.45%, 97.74% and 97.16% accuracy, respectively. The information in the data was successfully utilized by investigating the application of attention mechanisms and the precise position of the attention layer inside the deep learning model. This fascinating finding creates opportunities for more study on largescale EEG datasets and more reliable information extraction from massive data sets, ultimately allowing links to be made between brain activity and specific behaviours or task execution.

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Section: Discussionmentioning

confidence: 99%

“…classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG," Journal of Neural Engineering, vol. 20…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Attention-based Deep Learning Architectures for Classifying EEG in ADHD and Typical Children

Han,

Jin,

2024

IJACSA

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“…EEG signals possess distinct characteristics, including noise, weakness, nonlinearity, and non-stationarity, which vary among individuals [10]. Consequently, it is a significant challenge to identify robust patterns in EEG signals specific to a particular state.…”

Section: Introductionmentioning

confidence: 99%

Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition

2024

ijacsa

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In the current research, two nonlinear features were utilized for the design of EEG-based mental workload recognition: one feature based on differential entropy and the other feature based on multifractal cumulants. Clean EEGs recorded from 36 healthy volunteers in both resting and task states were subjected to feature extraction via differential entropy and multifractal cumulants. Then, these nonlinear features were utilized as input for a fuzzy KNN classifier. Experimental results showed that the multifractal cumulants feature vector achieved an AUC of 0.951, which is larger than the differential entropy feature vector (AUC = 0.935). However, the combination of both feature sets resulted in added value in identifying these two mental workloads (AUC = 0.993). Furthermore, the multifractal cumulants feature vector (best classification accuracy = 94.76%) obtained better classification results than the differential entropy feature vector (best classification accuracy = 92.61%). However, the combination of these two feature vectors achieved the best classification results: accuracy of 96.52%, sensitivity of 97.68%, specificity of 95.58%, and F1-score of 96.61%. This shows that these two feature vectors are complementary in identifying different mental workloads.

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“…Therefore, EEG signals have been targeted by computational neuroscientists and biomedical engineers for various biomedical applications [17][18][19][20][21][22][23]. So far, various EEG biomarkers have been introduced to diagnose psychiatric and neurological disorders [24,25]. Bosl et al used a combination of nonlinear EEG analysis and different machine learning techniques, achieving a 95% accuracy in screening pediatric populations at risk for autism [26].…”

Section: Introductionmentioning

confidence: 99%

Autism Diagnosis using Linear and Nonlinear Analysis of Resting-State EEG and Self-Organizing Map

Xu,

Yang

2023

IJACSA

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The prevalence of autism has increased dramatically in recent years and many people around the world are facing this difficult condition. There is a need to develop an objective method to diagnose autism. Various analysis methods have been used to classify the EEG signals of people with autism, from linear methods in the time and frequency domain to nonlinear methods based on chaos theory. However, there is still no consensus on which method of EEG signal analysis can provide us with the best diagnostic accuracy and valid biomarkers for autism diagnosis. Therefore, in this study, we evaluate different feature extraction methods from EEG signals to diagnose autism from healthy individuals. For this purpose, EEG analysis was performed in time, time-frequency, frequency and nonlinear domains. Furthermore, the self-organizing map (SOM) method was used to classify features extracted from autistic and normal EEG. The data used in this study were recorded by the research team from 24 children with autism and 24 normal children. The accuracies of 92.31, 93.57, 95.63 and 97.10% were achieved through time and morphological, frequency, time-frequency and nonlinear analyzes, respectively. Indeed, the findings showed that nonlinear analysis could yield the best classification results (accuracy = 97.10%, sensitivity = 98.80% and specificity = 97.02%) in the EEG discrimination of autistic children from typical children through the SOM neural network.

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Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals

Cited by 16 publications

References 33 publications

Assessment of Attention-based Deep Learning Architectures for Classifying EEG in ADHD and Typical Children

Assessment of Attention-based Deep Learning Architectures for Classifying EEG in ADHD and Typical Children

Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition

Autism Diagnosis using Linear and Nonlinear Analysis of Resting-State EEG and Self-Organizing Map

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