Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Ciprian, Claudio; Masychev, Kirill; Ravan, Maryam; Manimaran, Akshaya; Deshmukh, AnkitaAmol

doi:10.3390/a14050139

Cited by 18 publications

(6 citation statements)

References 65 publications

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“…Some researchers have studied brain connectivity maps for feature extraction. Ciprian et al [19] extracted features using the symbolic transfer entropy (STE) method and select using the Relief method. Various methods are used for classification; the best performance was achieved with KNN.…”

Section: Hand-engineered Techniquesmentioning

confidence: 99%

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: Hand-engineered Techniquesmentioning

confidence: 99%

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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“…While numerous EEG studies on SZ have utilized resting state data to uncover spontaneous brain activity and connectivity [8,[16][17][18][19][20], this paper shifts its focus towards a task-based EEG analysis. Although this approach presents challenges in patient work, it enables the exploration of specific altered cognitive processes in SZ, including working memory deficits and event-related potentials (ERPs) [21,22].…”

Section: Introductionmentioning

confidence: 99%

Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach

Iglesias-Parro,

Soriano,

Ibáñez-Molina

et al. 2023

Sensors

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Schizophrenia (SZ) is a complex disorder characterized by a range of symptoms and behaviors that have significant consequences for individuals, families, and society in general. Electroencephalography (EEG) is a valuable tool for understanding the neural dynamics and functional abnormalities associated with schizophrenia. Research studies utilizing EEG have identified specific patterns of brain activity in individuals diagnosed with schizophrenia that may reflect disturbances in neural synchronization and information processing in cortical circuits. Considering the temporal dynamics of functional connectivity provides a more comprehensive understanding of brain networks’ organization and how they change during different cognitive states. This temporal perspective would enhance our understanding of the underlying mechanisms of schizophrenia. In the present study, we will use measures based on graph theory to obtain dynamic and static indicators in order to evaluate differences in the functional connectivity of individuals diagnosed with SZ and healthy controls using an ecologically valid task. At the static level, patients showed alterations in their ability to segregate information, particularly in the default mode network (DMN). As for dynamic measures, patients showed reduced values in most metrics (segregation, integration, centrality, and resilience), reflecting a reduced number of dynamic states of brain networks. Our results show the utility of combining static and dynamic indicators of functional connectivity from EEG sensors.

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“…Machine learning (ML) algorithms are becoming a relevant tool to support the automatic detection of relevant information in EEG records. The objectives are diverse: recognition of emotions, evaluation of the sleep quality, and detection of epileptiform events, among others [2,[10][11][12][13][14][15][16]. Approaches to detect epileptiform events using ML include biomedical signal processing, analysis of characteristics extracted from the signals, and analysis of images in a lesser proportion [17].…”

Section: Introductionmentioning

confidence: 99%

Digital Transformation in Epilepsy Diagnosis Using Raw Images and Transfer Learning in Electroencephalograms

et al. 2022

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Epilepsy diagnosis is a medical care process that requires considerable transformation, mainly in developed countries, to provide efficient and effective care services taking into consideration the low number of available neurologists, especially in rural areas. EEG remains the most common test used to diagnose epilepsy. In recent years, there has been an increase in deep learning techniques to analyze electroencephalograms (EEG) to detect epileptiform events. These types of techniques support the epilepsy diagnostic processes performed by neurologists. There have been several approaches such as biomedical signal processing, analysis of characteristics extracted from the signals, and image analysis to detect epileptiform events. Most of the works reported in the literature, which use images, transformed the signals into a two-dimensional space interpreted as an image. However, only a few of them use the raw EEG image. This paper presents a computational model for detecting epileptiform events from raw EEG images, using convolutional neural networks and a transfer learning approach. To perform this work, 100 pediatric EEGs were collected, noting six characteristics of epileptiform events in each exam: spikes, poly-spikes, spike-and-wave, sharp waves, periodic, and a combination of them. Then, pre-trained convolutional neural networks were used, which, through transfer learning techniques, were retrained to classify possible events. The model’s performance was evaluated in terms of precision, accuracy, and Mathews’ correlation coefficient. The model offered a performance above 95% accuracy for binary classification and above 87% for multi-class classification. These results demonstrated that identifying epileptiform events from raw EEG images combined with deep learning techniques such as transfer learning is feasible. Significance: The proposed method for the evaluation of EEG tests, as a support tool for the diagnosis of epilepsy, can help to reduce the time of reading EEGs, which is very important, especially in developing countries with a limitation of a specialist in neurology.

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Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Cited by 18 publications

References 65 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

Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach

Digital Transformation in Epilepsy Diagnosis Using Raw Images and Transfer Learning in Electroencephalograms

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