Deep learning models for diagnosis of schizophrenia using EEG signals: emerging trends, challenges, and prospects
Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …
Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal
A brain connectivity network (BCN) is an advanced approach to examining brain
functionality in various conditions. However, the predictability of the BCN is affected by the …
functionality in various conditions. However, the predictability of the BCN is affected by the …
Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features
Recently, an increasing number of researchers have endeavored to develop practical tools
for diagnosing patients with schizophrenia using machine learning techniques applied to …
for diagnosing patients with schizophrenia using machine learning techniques applied to …
Schizophrenia Identification Through Deep Learning on Spectrogram Images
A Prabhakara Rao, G Prasanna Kumar… - … on Cognitive Computing …, 2023 - Springer
Schizophrenia (SZ) is one of the mental disorder due to which many people are suffering
around the world. People suffering with this disorder experience hallucinations, delusions …
around the world. People suffering with this disorder experience hallucinations, delusions …
Multi-modal neuroimaging feature fusion via 3D convolutional neural network architecture for schizophrenia diagnosis
B Masoudi, S Daneshvar… - Intelligent Data …, 2021 - content.iospress.com
Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the
quality of a patient's life and future treatments. Structural and functional neuroimaging …
quality of a patient's life and future treatments. Structural and functional neuroimaging …
EEG source network for the diagnosis of schizophrenia and the identification of subtypes based on symptom severity—A machine learning approach
JY Kim, HS Lee, SH Lee - Journal of Clinical Medicine, 2020 - mdpi.com
A precise diagnosis and a comprehensive assessment of symptom severity are important
clinical issues in patients with schizophrenia (SZ). We investigated whether …
clinical issues in patients with schizophrenia (SZ). We investigated whether …
[HTML][HTML] Multi feature fusion network for schizophrenia classification and abnormal brain network recognition
C Wang, C Wang, Y Ren, R Zhang, L Ai, Y Wu… - Brain Research …, 2024 - Elsevier
Schizophrenia classification and abnormal brain network recognition have an important
research significance. Researchers have proposed many classification methods based on …
research significance. Researchers have proposed many classification methods based on …
[PDF][PDF] Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification.
Signal processing based research was adopted with Electroencephalogram (EEG) for
predicting the abnormality and cerebral activities. The proposed research work is intended …
predicting the abnormality and cerebral activities. The proposed research work is intended …
Brain functional connectivity based on phase lag index of electroencephalography for automated diagnosis of schizophrenia using residual neural networks
H Polat - Journal of Applied Clinical Medical Physics, 2023 - Wiley Online Library
The complexity of symptoms of schizophrenia (SZ) complicate traditional and effective
diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time …
diagnoses based on clinical signs. Moreover, clinical diagnosis of SZ is manual, time …