ArticleJournal of medical and biological engineering2022
Mental Disorder Diagnosis from EEG Signals Employing Automated Leaning Procedures Based on Radial Basis Functions.
Article in Journal of medical and biological engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- A novel strategy for enhanced schizophrenia detection using established CNN architectures.BMC medical imaging · 2025Article
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- ViT-Based Face Diagnosis Images Analysis for Schizophrenia Detection.Brain sciences · 2024Article
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Authors and funding
6 authors.
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Abstract
Purpose: In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented. Methods: To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm. Results: The results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. Conclusion: The proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.