Advanced Signal Processing for Real-Time Seizure Detection Using Wearable EEG Electronics in Nigerian Clinical Contexts

Background: Epilepsy affects over 4 million Nigerians and inadequate seizure monitoring in resource-limited clinical settings leads to suboptimal medication management. Wearable EEG electronics with real-time seizure detection represent a transformative but technically demanding solution requiring advanced signal processing tailored to Nigerian clinical and demographic contexts. Aim: This study developed advanced signal processing algorithms for real-time seizure detection on a wearable EEG electronics platform, validated in Nigerian epilepsy patients. Methods: A 16-channel dry electrode EEG headset with embedded STM32H7 digital signal processing was designed. Signal processing pipeline stages included adaptive artefact removal, wavelet-based feature extraction, and an ensemble classifier incorporating random forest, SVM, and LSTM deep learning components. The classifier was trained on EEG datasets from 120 Nigerian epilepsy patients at Lagos University Teaching Hospital. Real-time detection performance was evaluated prospectively in 35 patients during continuous monitoring. Results: Seizure detection sensitivity was 95.8% with false alarm rate of 0.15 per hour. Mean detection latency was 3.4 seconds from seizure onset. The embedded processor executed the complete pipeline within 48 milliseconds per 1-second EEG window. Battery life with all channels active was 18 hours. Conclusion: The wearable EEG platform with advanced signal processing achieves clinical-grade seizure detection performance suitable for Nigerian hospital and ambulatory monitoring applications. Keywords: EEG, seizure detection, wearable electronics, signal processing, epilepsy Nigeria.