Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal

Bagherzadeh, Sara; Shahabi, Mohsen Sadat; Shalbaf, Ahmad

doi:10.1016/j.compbiomed.2022.105570

Cited by 40 publications

(13 citation statements)

References 49 publications

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“…Several factors, including the number of EEG trials, the type and number of microstate features (i.e., MsMC), and the feature selection method (i.e., feature importance score calculation) may have contributed to this difference. The accuracy, specificity, and sensitivity scores reported here are comparable with some deep learning approaches that use EEG microstates for SZ classification [ 68 , 69 , 70 , 71 , 72 , 73 ]. The fact that findings obtained from our optimized ML approach were comparable to deep learning methods in terms of performance but outperformed in computation and time of processing (i.e., our method [order of minutes] vs. [order of hours]) potentially provide a more rapid, efficient way to achieve an optimized SZ classification.…”

Section: Discussionsupporting

confidence: 68%

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

et al. 2022

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Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls’ EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.

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Section: Discussionsupporting

confidence: 68%

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

et al. 2022

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“…Table 2 provides a comprehensive summary of various studies that have used deep learning methods for the detection of SCZ using EEG. The method presented in Bagherzadeh et al (2022) employ Butterworth filters and Transfer Entropy (TE) in conjunction with various deep learning architectures, including VGG-16, ResNet50V2, InceptionV3, EfficientNetB0, DenseNet121, and CNN-LSTM, achieving an impressive accuracy of 99.90% on EEG signals from the IPN database. Aslan and Akin (2022) employs CWT and advanced CNN and VGG16, achieving a high accuracy of 99.5% on both private and IPN EEG data.…”

Section: Schizophrenia Classification Using Deep Learningmentioning

confidence: 99%

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Rahul,

Sharma,

Sharma

et al. 2024

Front. Hum. Neurosci.

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The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI’s potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

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“…This is because EEG has portability advantages, lower cost, better tolerability, and higher signal temporal resolution than functional magnetic resonance imaging. It is also an established modality that has been applied in the clinic for diagnosing mental disorders or neurological conditions [9,[13][14][15][16]. Many researchers have developed machine learning methods to classify emotions using EEG.…”

Section: Introductionmentioning

confidence: 99%

“…For example, CNN was combined with a sparse autoencoder and deep neural network to achieve the accuracy of 89.49% and 92.86% to discriminate valence and arousal classes, respectively [25]. Some studies [15,16,30] showed the effectiveness of adding time dependency by combining CNNs and long short-term memory (LSTM). This combination has increased the classification accuracy of EEG signals in neuroscience.…”

Section: Introductionmentioning

confidence: 99%

“…TE is based on the information theory and estimates the nonlinear relationships of transfer of information between pairs of EEG channels. In other words, TE is a model-free, non-parametric and nonlinear effective connectivity measure that estimate causality between pairs of two brain regions according to the conditional entropy [16]. Whereas PDC and dDTF are linear measures and, consequently, more restricted.…”

Section: Introductionmentioning

confidence: 99%

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Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

Bagherzadeh,

Shalbaf,

Shoeibi

et al. 2024

IEEE Access

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The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out crossvalidation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pretrained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

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Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal

Cited by 40 publications

References 49 publications

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

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