| Summary: | An accurate diagnosis of schizophrenia is difficult; no reliable biomarkers of the disease exist.
We present a computational approach for diagnosis of schizophrenia from electroencephalography (EEG) recordings.
Novel and existing mathematical methods for the interpretation of EEG are surveyed and compared.
Methods utilizing single electrodes are used in conjunction with those incorporating the recordings of multiple electrodes.
A data-driven, machine-learning approach is used to automate the selection of relevant features, which are then classified using least-squares support vector machines.
This approach yielded a prediction accuracy of 86.5%, using a stringent application of correct statistical techniques.
Those features deemed most relevant are related with known abnormalities symptomatic of schizophrenia.
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