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 …

Analysing complexity, variability and spectral measures of schizophrenic EEG signal

M Sabeti, R Behroozi, E Moradi - International Journal of …, 2016 - inderscienceonline.com
Symptoms, signs and disease progression are the mainstay of psychiatric disorders
diagnosis but defining a biomarker would be a more accurate way for their diagnosis in …

Pycaret for the evaluation of classification methods in order to set up a decision making system for the early diagnosis of schizophrenia by EEG

PFT Nanfack, ET Fute, PA Ele - 2022 16th International …, 2022 - ieeexplore.ieee.org
According to the World Health Organization, approximately 24 million people suffer from
schizophrenia worldwide. However, there is no clinical examination to diagnose this …

Automated detection of schizophrenia using nonlinear signal processing methods

V Jahmunah, SL Oh, V Rajinikanth, EJ Ciaccio… - Artificial intelligence in …, 2019 - Elsevier
Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to
predict abnormality and cerebral activities. The purpose of this study was to develop an …

[HTML][HTML] Method for classifying schizophrenia patients based on machine learning

C Soria, Y Arroyo, AM Torres, MÁ Redondo… - Journal of Clinical …, 2023 - mdpi.com
Schizophrenia is a chronic and severe mental disorder that affects individuals in various
ways, particularly in their ability to perceive, process, and respond to stimuli. This condition …

Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent …

N Shaffi, M Mahmud, F Hajamohideen… - … on Information and …, 2022 - Springer
The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum
of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate …

Recognition of Schizophrenia Patients by EEG Signal Using the STFT/CWT Layer with Group-Convolution

VQ Huynh, H Do Tran… - 2023 RIVF International …, 2023 - ieeexplore.ieee.org
The classic method of diagnosing schizophrenia involves a qualified psychiatrist
interviewing the patient. Furthermore, a severe psychological condition significantly harms …

Examining reproducibility of EEG schizophrenia biomarkers across explainable machine learning models

CA Ellis, A Sattiraju, R Miller… - 2022 IEEE 22nd …, 2022 - ieeexplore.ieee.org
Schizophrenia (SZ) is a neuropsychiatric disorder that adversely effects millions of
individuals globally. Current diagnostic efforts are symptom based and hampered due to the …

Integrating Multi-scale Feature Representation and Ensemble Learning for Schizophrenia Diagnosis

M Xiao, H Kuang, J Liu, Y Zhang… - … on Bioinformatics and …, 2022 - ieeexplore.ieee.org
Resting-state functional magnetic resonance imaging (rs-fMRI) images have been widely
used for diagnosis of schizophrenia. With rs-fMRI, most existing schizophrenia diagnostic …

Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework

Z Guo, J Wang, T Jing, L Fu - Computer Methods and Programs in …, 2024 - Elsevier
Background and objective Electroencephalogram (EEG) signals record brain activity, with
growing interest in quantifying neural activity through complexity analysis as a potential …