MINI REVIEW article
Front. Neurol.
Sec. Neurotechnology
Artificial Intelligence in Seizure Detection Devices: Current Technologies and Future Directions
Tiffany Jiaqi Ho 1
Bridget Elaine LaMonica Ostrem 2
James Michael Hillis 3
1. University of California Berkeley, Berkeley, United States
2. University of California San Francisco, San Francisco, United States
3. Mass General Brigham Inc, Boston, United States
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Abstract
Epilepsy affects millions of people worldwide, driving the need for advanced methods to monitor patients’ health and seizure activity. Recent advances in wearable technologies have enabled continuous collection of physiological data to support real-time seizure detection in the real-world. This review presents a targeted synthesis of 23 studies evaluating wearable devices and their associated artificial intelligence (AI) algorithms for automated seizure detection. Both wrist- and ear-based systems demonstrate high sensitivity, with performance influenced by device design, signal reliability, and analytic approach. The main challenges include reducing false alarms and maintaining data integrity during everyday use. More recent studies highlight the ability to anticipate seizures before they occur, marking a promising step toward improving safety and well-being for people living with epilepsy. Ongoing efforts to identify reliable physiological markers and to evaluate device performance across diverse populations are key to integrating wearable technologies for seizure detection into routine medical care.
Summary
Keywords
artificial intelligence, Epilepsy, machine learning, seizure, wearable
Received
29 November 2025
Accepted
03 February 2026
Copyright
© 2026 Ho, Ostrem and Hillis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tiffany Jiaqi Ho
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.