Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm

Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are developed to supplement the often inaccurate interview-based risk assessments. Our research goal is to develop a machine learning algorithm (MLA) to predict the presence of suicide ideation from resting state electroencephalography (EEG) data collected from 224 subjects with major depressive disorder (MDD) in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study. Using the Concise Health Risk Tracking Self-Report (CHRT-SR14) questionnaire, 194 subjects acknowledged having suicidal ideation (group 1) and 30 did not (group 2). We balanced the database by matching 30 subjects from group 1 using propensity score analysis. A four-step prediction algorithm was then applied to the selected data including 1) EEG data preprocessing, 2) brain source localization (BSL) using the robust exact low-resolution electromagnetic tomography (ReLORETA) method, 3) determining the connectivity between the brain regions using symbolic transfer entropy (STE), 4) applying MLA to the STE features. Three common classifiers, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used in this study. Using 70% of the data for training and evaluation and 30% for testing, all three classifiers delivered a high accuracy, where the highest performance belonged to SVM with 88.9% accuracy. These findings support the potential utility of ML analysis of EEG data as a non-verbal way to enhance the accuracy of suicide risk evaluation.