Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

Abstract

Schizophrenia (SZ) is a neuropsychiatric disorder that adversely effects millions of individuals globally. Current diagnostic efforts are symptom based and hampered due to the variability in symptom presentation across individuals and overlap of symptoms with other neuropsychiatric disorders. This spawns the need for (1) biomarkers to aid with empirical SZ diagnosis and (2) the development of automated diagnostic approaches that will eventually serve in a clinical decision support role. In this study, we train random forest (RF) and support vector machine (SVM) models to differentiate between individuals with schizophrenia and healthy controls using spectral features extracted from resting state EEG data. We then perform two explainability analyses to gain insight into key frequency bands and channels. In our explainability analyses, we examine the reproducibility of SZ biomarkers across models with the goal of identifying those that have potential clinical implications. Our model performance results are well above chance level indicating the broader utility of spectral information for SZ diagnosis. Additionally, we find that the RF prioritizes the upper {gamma}-band and is robust to loss of information from individual electrodes, while the SVM prioritizes the and {theta}-bands and P4 and T8 electrodes. It is our hope that our findings will inform future efforts towards the empirical diagnosis of SZ and towards the development of clinical decision support systems for SZ diagnosis.