Meta-learning on Spectral Images of Electroencephalogram of Schizophenics

TL;DR

This paper introduces a meta-learning approach using convolutional neural networks to analyze spectral EEG images for diagnosing schizophrenia, aiming to improve early detection and clinical decision-making.

Contribution

It develops novel classifiers employing Model-Agnostic Meta-Learning and prototypical networks for accurate schizophrenia diagnosis from spectral EEG images.

Findings

Classifiers effectively distinguish schizophrenia patients from healthy controls.

Meta-learning enhances classifier adaptability to new patient data.

Proposed methods outperform traditional EEG analysis techniques.

Abstract

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.