AI-Enabled Delirium Detection Using Portable EEG and Topological Data Analysis

Delirium is one of the most common, dangerous, and underdiagnosed conditions affecting hospitalized older adults worldwide. In routine clinical practice, detection relies largely on repeated questionnaire-based assessments, which are difficult to apply consistently by busy healthcare staff and show poor sensitivity in real-world settings (reported 38-47% in busy clinical environments, including intensive care units). As a result, delirium is frequently missed or detected late, despite being strongly associated with mortality, prolonged hospitalization, institutionalization after discharge, and long-term cognitive decline. The burden extends beyond individual patients, imposing substantial systemic costs, reported to exceed USD 150 billion annually in the United States alone. While conventional multi-lead EEG can detect delirium-related diffuse brain slowing, it is impractical for large-scale screening due to equipment complexity, specialist staffing requirements, and delays in interpretation. Earlier EEG-based algorithmic approaches, including simplified spectral methods, demonstrated feasibility but were limited in accuracy due to reliance on frequency features alone. This case addresses this global gap by introducing Topological Data Analysis (TDA), a fundamentally different and interpretable AI approach that captures global temporal irregularities in EEG signals, combined with a simplified portable EEG method, to enable objective, scalable, and clinically practical delirium detection using realworld hospital data.