2021/01/27 by Maritza Tynes, Tynes, Maritza, Mahboobeh Parsapoor +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.2101.12208
openalex publication_date 2021/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Schizophrenia is a complex psychiatric disorder involving changes in thought\npatterns, perception, mood, and behavior. The diagnosis of schizophrenia is\nchallenging and requires that patients show two or more positive symptoms for\nat least one month. Delays in identifying this debilitating disorder can impede\na patient ability to receive much needed treatment. Advances in neuroimaging\nand machine learning algorithms can facilitate the diagnosis of schizophrenia\nand help clinicians to provide an accurate diagnosis of the disease. This paper\npresents a methodology for analyzing spectral images of Electroencephalography\ncollected from patients with schizophrenia using convolutional neural networks.\nIt also explains how we have developed accurate classifiers employing\nModel-Agnostic Meta-Learning and prototypical networks. Such classifiers have\nthe capacity to distinguish people with schizophrenia from healthy controls\nbased on their brain activity.\n