Forecasting and real-time detection of neonatal seizures: A machine learning perspective

Recognition and treatment of neonatal seizures, primarily diagnosed using EEG, are essential to protect the developing brain, yet only 11% of seizures are treated within 1 hour of onset. Developing seizure forecasting and detection systems could help address this issue in intensive care settings with limited neurologist availability.

We propose an ML architecture with dual functionality: (1) forecasting seizures within short prediction intervals (5, 10, and 15 min), and (2) detecting seizures in real time, using explainable entropy-, singular value decomposition-, power spectrum-, and statistical moment-based features with an AdaBoost classifier. The same feature set supports both functionalities, with forecast reliability improving when features are derived from longer EEG intervals. The model was trained and tested on a single-channel (P3-P4, n = 82) dataset and a multi-channel public dataset (n = 79).

The proposed ML model was able to forecast seizures at a 15-min prediction interval (86.6%/92.4% sensitivity/specificity; Matthews correlation coefficient, MCC = 0.39), with better performance at the shorter 5-min prediction interval (95.6%/88.9% sensitivity/specificity; MCC = 0.55) for the single-channel EEG seizure group. Short-range forecasting outperformed the pioneering study on the public multi-channel EEG dataset (MCC = 0.48). The proposed ML model achieved over a 30% improvement in MCC and Pearson's coefficient on the single-channel dataset compared to the state-of-the-art deep-learning architecture (ConvNeXt).

EEG segmentation and appropriate feature selection enhance ML model performance for seizure detection in small datasets. Validation of feature-based ML models is required on both single and multi-channel EEG, given that quantitative features can be sensitive to different bipolar channel pairs. Although the method has outperformed the pioneering studies in seizure forecasting, further improvement in performance and more extensive validation are needed for clinical adoption.