EEG-based hypoglycemia detection in Type 1 Diabetes: Proof-of-concept study

Early detection of hypoglycemia among non-hypoglycemic conditions is critical in type 1 diabetes (T1D), as delayed intervention can lead to serious neurological and metabolic consequences. Although continuous glucose monitoring (CGM) systems provide continuous glucose measurements, they are invasive and do not capture early neurophysiological alterations associated with hypoglycemia-related metabolic changes. This study presents a preliminary feasibility investigation of a non-invasive brain-computer interface approach for classifying hypoglycemia versus non-hypoglycemia in individuals with type 1 diabetes using electroencephalography (EEG). The novelty lies in demonstrating EEG-based hypoglycemia detection (binary classification) under data-limited conditions using optimized segmentation and lightweight classifiers. Additionally, EEG characteristics were compared with recordings from five healthy controls to provide a baseline reference for spectral activity patterns. EEG recordings from participants with T1D were synchronized with continuous glucose monitoring (CGM) data and pre-processed using a 0.5-50 Hz band-pass filter. The control group underwent identical EEG pre-processing without CGM synchronization. Reproducible spectral patterns were identified: hypoglycemia was associated with characteristic delta and beta alterations, while changes observed outside hypoglycemia were less consistent and did not support reliable separation within the non-hypoglycemic class. Multiple segmentation strategies and data-efficient machine learning models were evaluated under limited data conditions. Classical classifiers demonstrated promising within-subject performance under severely data-limited conditions, with Quadratic Discriminant Analysis (QDA) achieving the best results (accuracy 0.96212, macro-F1 0.96201) for binary classification focused on hypoglycemia detection. Confusion matrix analysis indicated a low rate of clinically relevant misclassifications. These findings support the feasibility of lightweight, real-time, non-invasive EEG-based systems for early hypoglycemia detection.