Global Entrepreneurship Summit 2026

Seizures are a major health concern among many children with autism spectrum disorder, requiring timely detection and early warning to reduce risks and support caregivers. This project presents an autism-cantered, AI-based seizure prediction system that uses multimodal physiological and movement signals, including EEG, heart rate variability, motion activity, posture changes, and skin temperature. The system analyses real-time bio signal patterns through lightweight on-device intelligence to identify early seizure-related changes before a critical event occurs. By combining multiple sensor inputs, the proposed approach improves prediction reliability, reduces false alerts, and supports continuous health monitoring without depending entirely on internet connectivity. When high-risk patterns are detected, instant alerts can be sent to caregivers or healthcare support systems for early intervention. This solution aims to improve safety, enhance quality of life, and provide continuous care support for autistic children who are vulnerable to seizure-related emergencies.

Dr. Rizwana Naz Asif is an outstanding educator and researcher who has a Ph.D. in Computer Science, focusing on utilizing AI in healthcare applications. Having completed 2 MSc degrees in Robotics at Middlesex University and Management at BPP University she is a technically innovative leader. She has an MSCS Bioinformatics degree and an MCS in Hajvery University, with a sound base in life sciences. She has written extensively in leading journals and has more than 20 years of teaching experience in Pakistan and the UK and is a devoted advocate of education, health, and AI in the future, through the use of technology.