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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651124 | PMC |
| http://dx.doi.org/10.1007/s40846-022-00758-9 | DOI Listing |
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