Classification of Schizophrenia EEG recording using homological features

Identifying mental disorders through EEG-based biomarkers holds significant clinical value. However, the small number of patients and the high variability of individual data pose challenges in their development. Existing solutions are either not sufficiently accurate or overly complicated for practical clinical use. Topological Data Analysis (TDA) offers a potential method for deriving robust features from intricate time series. This study explores various EEG signal embeddings, generating TDA features for the classification of cases of schizophrenia in adolescents. Just two features were sufficient to achieve an accuracy of 80% in a 5-fold cross-validation, offering a straightforward and insightful model for diagnosing schizophrenia.