Clinically Calibrated Machine Learning Benchmarks for Large-Scale Multi-Disorder EEG Classification

TL;DR

This paper develops and evaluates machine learning models for automated classification of eleven neurological disorders using EEG data, emphasizing sensitivity and clinical relevance in real-world settings.

Contribution

It introduces a multi-disorder EEG classification framework with sensitivity-focused calibration, addressing class imbalance and supporting scalable clinical screening.

Findings

Achieved over 80% recall for most disorders.

Significant recall improvements (15-30%) for low-prevalence conditions.

Feature importance aligns with known clinical EEG markers.

Abstract

Clinical electroencephalography is routinely used to evaluate patients with diverse and often overlapping neurological conditions, yet interpretation remains manual, time-intensive, and variable across experts. While automated EEG analysis has been widely studied, most existing methods target isolated diagnostic problems, particularly seizure detection, and provide limited support for multi-disorder clinical screening. This study examines automated EEG-based classification across eleven clinically relevant neurological disorder categories, encompassing acute time-critical conditions, chronic neurocognitive and developmental disorders, and disorders with indirect or weak electrophysiological signatures. EEG recordings are processed using a standard longitudinal bipolar montage and represented through a multi-domain feature set capturing temporal statistics, spectral structure, signal complexity, and inter-channel relationships. Disorder-aware machine learning models are trained under severe class imbalance, with decision thresholds explicitly calibrated to prioritize diagnostic sensitivity. Evaluation on a large, heterogeneous clinical EEG dataset demonstrates that sensitivity-oriented modeling achieves recall exceeding 80% for the majority of disorder categories, with several low-prevalence conditions showing absolute recall gains of 15-30% after threshold calibration compared to default operating points. Feature importance analysis reveals physiologically plausible patterns consistent with established clinical EEG markers. These results establish realistic performance baselines for multi-disorder EEG classification and provide quantitative evidence that sensitivity-prioritized automated analysis can support scalable EEG screening and triage in real-world clinical settings.