Enhancing Seizure Detection Through Deep Learning-Based Wearable Devices

This study focuses on harnessing data from diverse sensors, such as Electrodermal Activity, Accelerometer, Heart Rate, and Blood Volume Pulse, aiming to construct a sophisticated deep learning model tailored for precise seizure detection. By discerning subtle seizure patterns within multimodal sensor data, the envisioned model undergoes rigorous training on a comprehensive dataset containing labeled seizure and non-seizure instances. This model's integration into wearable devices promises to address the critical concern of Sudden Unexpected Death in Epileptic Seizures (SUDEP) by facilitating swift responses and interventions. The ultimate aim is to seamlessly integrate a state-of-the-art deep learning model into wearable devices, thereby revolutionizing seizure management, reducing the risk of SUDEP, and fundamentally improving patient outcomes and quality of life. Empowering caregivers and healthcare professionals with timely insights could profoundly reshape the landscape of epilepsy care and significantly improve patient well-being.