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

• URL: http://arxiv.org/abs/2512.22656v1
• Date: Sat, 27 Dec 2025 17:11:17 GMT
• Title: Clinically Calibrated Machine Learning Benchmarks for Large-Scale Multi-Disorder EEG Classification
• Authors: Argha Kamal Samanta, Deepak Mewada, Monalisa Sarma, Debasis Samanta,
• Abstract summary: This study examines automated EEG-based classification across eleven clinically relevant neurological disorder categories.<n>Machine learning models are trained under severe class imbalance, with decision thresholds explicitly calibrated to prioritize diagnostic sensitivity.
• Score: 6.941409613662483
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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