# Artificial intelligence in wearable seizure detection devices: current technologies and future directions

## TL;DR

This paper reviews current wearable devices and AI algorithms for detecting seizures, highlighting their potential and challenges in real-world use.

## Contribution

The paper provides a synthesis of 23 studies on wearable seizure detection technologies and identifies future directions for improving their reliability and performance.

## Key findings

- Wrist- and ear-based wearable systems show high sensitivity for seizure detection.
- False alarms and data integrity remain significant challenges in real-world use.
- Recent studies demonstrate the potential for seizure anticipation before onset.

## 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.