Schizophrenia is a complex psychiatric disorder involving changes in thought patterns, perception, mood, and behavior. The diagnosis of schizophrenia is challenging and requires that patients show two or more positive symptoms for at least one month. Delays in identifying this debilitating disorder can impede a patient ability to receive much needed treatment. Advances in neuroimaging and machine learning algorithms can facilitate the diagnosis of schizophrenia and help clinicians to provide an accurate diagnosis of the disease. This paper presents a methodology for analyzing spectral images of Electroencephalography collected from patients with schizophrenia using convolutional neural networks. It also explains how we have developed accurate classifiers employing Model-Agnostic Meta-Learning and prototypical networks. Such classifiers have the capacity to distinguish people with schizophrenia from healthy controls based on their brain activity.
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