Schizophrenia Detection Using EEG: A Study on Frequency Relevance

One important application of machine learning is the analysis of electroencephalographic recordings (EEG). Such oscillatory signals are noisy, non-stationary, full of artifacts, contain transients, and chaotic transitions between meta-stable states. EEG data rarely consists of recordings obtained for more than 100 patients with some brain disorder. This makes objective, reliable clinical diagnosis of many mental disorders, such as schizophrenia, very difficult. The standard approach splits the frequency of EEG oscillations focusing on five classical bands: delta, theta, alpha, beta, and gamma. In this paper, we investigate three main questions: (i) Are there certain frequencies that will allow for a reliable diagnosis of schizophrenia, and (ii) if they are, is there a connection to what is known about their neural basis? (iii) How long segments of EEG recordings are sufficient for reliable classification? We filter EEG signals, obtaining a set of 64 very narrow 1Hz frequency bands. A genetic algorithm, with a simple k-NN classifier, finds an optimal combination of these bands. The method is sufficiently simple to be used in clinical settings. A public schizophrenia EEG data set, containing 60 seconds of EEG recordings in the resting state, with only 16 electrodes, for 45 adolescent patients and 39 healthy controls, was used for testing. Signal duration of about 30 seconds enables to reach an accuracy of over 96% in 5-fold cross-validation tests. We compare our results to much more sophisticated state-of-the- art methods and discuss insights gained by our analysis into the brain basis of schizophrenia.