Real-Time Seizure Prediction from EEG Signals Using AI and Wearable Technology: An Integrated Approach for Proactive Epilepsy Management

Epilepsy is a condition that impacts more than 50 million individuals globally, with uncontrolled seizures causing patient safety and quality-of-life concerns. In this work, we explain the real-time seizure prediction system that combines electroencephalography (EEG) signal processing, machine learning algorithms and wearable technology to enable proactive management of epilepsy. Our system uses Support Vector Machine (SVM) classification with 80% accuracy for detecting the preictal state, which is better than logistic regression (72%) and random forest (77%) methods. The system uses Huffman coding to offer efficient data compression with a ratio of 4.36:1, allowing real-time transmission of EEG signals through TCP/IP network protocols. The wearable device architecture offers seizure warn- ings in real time, revolutionizing epilepsy care from reactive hospital-oriented to proactive, continuous care. Validation with the Siena Scalp EEG Database shows the system to detect pre- ictal patterns, which is a real-world solution for seizure prediction in the ambulatory setting. This paper promotes the confluence of neuroscience, signal processing and computational biology, which offers a platform for next-generation epilepsy management systems.