Comprehensive Summary
The study presents a new approach to detecting seizures from EEG recordings by introducing a model called SC-LSTM, which is designed to address the difficulties of noisy, high-dimensional, and highly variable brain signals. Instead of relying on conventional feature extraction, the method incorporates a self-calibration component that adapts to differences across electrode channels, while a bidirectional recurrent layer is used to track how patterns evolve over time. This combination allows the system to handle missing or corrupted inputs more effectively than standard convolutional or hybrid models. When applied to two neonatal EEG datasets, the framework consistently outperformed existing baselines, reaching near-perfect accuracy and discriminative power. Even under conditions where important electrodes were removed, the model sustained strong detection performance, showing its robustness to data loss. By merging adaptive spatial analysis with temporal tracking, SC-LSTM offers a dependable solution for seizure monitoring and could be valuable in clinical environments where the quality of EEG signals often varies.
Outcomes and Implications
This work has important implications for clinical practice, as it points toward more dependable seizure monitoring systems that can be integrated into both hospital and home settings. Because SC-LSTM can maintain high accuracy even when electrode channels are missing or signals are noisy, it could reduce the need for constant manual review by neurologists and help ensure that seizures are identified quickly in real time. Such reliability is especially valuable for neonatal intensive care, where continuous EEG surveillance is critical but staff resources are limited. Beyond newborns, the approach could also be extended to adults with epilepsy, enabling more personalized monitoring tools that adapt to individual brain activity patterns. In the longer term, the framework may support closed-loop therapeutic devices, where rapid and accurate seizure detection can trigger responsive interventions such as electrical stimulation or drug delivery, thereby improving patient outcomes and quality of life.