• Corpus ID: 238634759

EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia

@article{Alves2021EEGFC,
  title={EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia},
  author={Caroline Lourenco Alves and Aruane Mello Pineda and Kirstin Roster and Christiane Thielemann and Francisco A. Rodrigues},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06140}
}
Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis, but the absence of established clinical tests makes this task challenging. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that… 
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References

SHOWING 1-10 OF 62 REFERENCES
Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by…
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
TLDR
An automated detection system for Parkinson’s disease employing the convolutional neural network (CNN) employing the thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is proposed.
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks
TLDR
A deep convolutional neural network can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis and outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease
TLDR
A unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction is suggested.
Automated EEG-based screening of depression using deep convolutional neural network
TLDR
It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere, consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere.
Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings
TLDR
It is shown that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic and nonschizophrenic persons with a very good balanced accuracy.
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease
TLDR
Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.
Automated detection of schizophrenia using nonlinear signal processing methods
TLDR
An Automated Diagnostic Tool to investigate and classify the EEG signal patterns into normal and schizophrenia classes is developed and the experimental outcome showed that the SVM with Radial-Basis-Function (SVM-RBF) offered a superior average performance value on the considered EEG dataset, as compared to other classifiers implemented in this work.
EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease
TLDR
Electroencephalogram has the potential to be a non-invasive functional biomarker that predicts progression from MCI to AD and microstates associated with the frontoparietal network were altered in AD.
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
TLDR
In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes and achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
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