Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network

Shen, Mingkan; Wen, Peng; Song, Bo; Li, Yan

doi:10.1016/j.compbiomed.2023.107022

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…It integrated the feature extraction and classification modules in a unified learning framework, and because of this, it learned discriminative features in a better way and results in better performance. In addition, our method employed EEG signal trials of 3 s, and as such, it is more efficient than the methods in [11][12][13]16,21,27]. The complexities of the deep models used by Tynes et al [14] and Aslan et al [15] are very high, which are 5,048,898 and 138 million learnable parameters; they are difficult to train or fine-tune, avoiding overfitting using the available dataset.…”

Section: Differences With Related Methodsmentioning

confidence: 99%

“…Ko et al [26] adopted Recurrence Plot (RP) and Gramian Angular Field (GAF) methods to transform the EEG signal into an image that was fed to VGGNet. A combination of functional connectivity theory and deep learning model was employed by Shen et al [27] to classify the EEG signal. The frequency bands of the EEG signal were extracted using continuous wavelet transform (CWT).…”

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

“…A close look at the existing methods reveals that, firstly, the complex deep models were used with relatively small datasets, which is an indicator of the overfitting problem; the methods in [11,14,15,22,24,25] achieved accuracies of 98.07%, 99%, 99.25%, 99.9%, 96.83%, and 99.5%, respectively. Moreover, some studies [11][12][13][14]16,21,22,24,27] used EEG trials of long temporal lengths in a range between 20 to 60 s, which requires more computation and time. Also, in [23], the authors adopted only two channels, FP1 and FP2, without providing enough information on why they chose them.…”

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

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Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network

Hussain,

Alsalooli,

Almaghrabi

et al. 2024

Applied Sciences

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Schizophrenia is a chronic mental disorder that affects millions of people around the world. Neurologists commonly use EEG signals to distinguish schizophrenia patients from normal controls, but their manual analysis is tedious and time-consuming. This has motivated the need for automated methods based on machine learning. However, the methods based on hand-engineered features need human experts to decide which features should be extracted. Though deep learning has recently shown good results for schizophrenia detection, the existing deep models have high parameter complexity, making them prone to overfitting because the available data are limited. To overcome these limitations, we propose a method based on an ensemble-like approach and a lightweight one-dimensional convolutional neural network to discriminate schizophrenia patients from healthy controls. It splits an input EEG signal for analysis into smaller segments, where the same backbone model analyses each segment. In this way, it makes decisions after scanning an EEG signal of any length without increasing the complexity; i.e., it scales well with an EEG signal of any length. The model architecture is simple and involves a small number of parameters, making it easy to implement and train using a limited amount of data. Though the model is lightweight, enough trials are still needed to learn the discriminative features from available data. To tackle this issue, we introduce a simple data augmentation scheme. The proposed method achieved an accuracy of 99.88% on a public benchmark dataset; it outperformed the state-of-the-art methods. It will help neurologists in the rapid and accurate detection of schizophrenia patients.

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Section: Differences With Related Methodsmentioning

confidence: 99%

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network

Hussain,

Alsalooli,

Almaghrabi

et al. 2024

Applied Sciences

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“…Notably, ( Bao et al, 2023 ) attained the highest accuracy of 99.57% using Notch Filter, PCA, CNN, and TCNs. Shen et al (2023) employed Continuous Wavelet Transform and a 3D-CNN, achieving 98.89%. The highest accuracy was reported by Bagherzadeh et al (2022) , achieving an impressive 99.90% and the other hand, the lowest accuracy was noted in the study ( Phang et al, 2019 ), with an accuracy of 91.69%.…”

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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“…In our previous work, 3D-CNN classifier was proposed to classify the EEG alcoholic brain connectivity data and received the results of 96.25 ± 3.11 % accuracy [ 22 ]. Moreover, this kind of method also employed in our previous research [ 23 ]. Our 3D-CNN method provided the 97.74 ± 1.15 % accuracy, 96.91 ± 2.76 % sensitivity, and 98.53 ± 1.97 % results.…”

Section: Introductionmentioning

confidence: 99%

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Shen,

Yang,

Wen

et al. 2024

Heliyon

Self Cite

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Automatic identification of schizophrenia based on EEG signals using dynamic functional connectivity analysis and 3D convolutional neural network

Cited by 10 publications

References 29 publications

Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network

Schizophrenia Detection on EEG Signals Using an Ensemble of a Lightweight Convolutional Neural Network

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

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

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