STTR Phase I: Patient-Specific System for Early Detection and Identification of Epileptic Seizures

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to provide epileptic patients, and their caregivers a smart system that can predict seizures before they occur.There are more than 3 million adults and 1 million children in the US, and more than 50 million people worldwide, suffering from epilepsy.Repeated and unpredictable seizures significantly affect the quality of life of people suffering from epilepsy. These seizures remain the leading cause of economic, emotional, and physical injuries for people with epilepsy and their caregivers. Design, development, and integration of artificial intelligence (AI) models with instruments that detect abnormalities in brain waves like electroencephalogram (EEG) for real-time seizure prediction may bring improvements for these patients and their caregivers. This technology is poised to capture a portion of the rapidly growing $6 billion US market of AI healthcare solutions._x000D_ _x000D_ This Small Business Technology Transfer (STTR) Phase I project supports the development of a novel consumer product that works with caregivers to proactively mitigate the risk of seizure events in people with epilepsy. Current commercial solutions are mostly reactive, and support is available only after a seizure event. The company will fill this gap by developing, testing, integrating, and evaluating machine learning (ML) models - applied to EEG data - for epileptic seizure prediction. The scientific approach will leverage inherently heterogenous and complex edge technologies. Data connectivity with third party vendor EEG caps, microcontrollers, smart phones, and cloud services rely on many different operational technologies and communication standards. This research will overcome these challenges with hardware and software solutions that will integrate these services within an edge device to enable application portability and simplify deployment. Challenges such as inference on limited computational power and energy devices, and its effects on the accuracy/sensitivity of the predictions will be solved using robust cross-validation techniques, extensive testing, and benchmarking using community standards. The technical product of this research will advance caregiver knowledge and increase understanding of epileptic seizures as well as increase patient well-being._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.