JMIR Publications

ABSTRACT

Background:

Epilepsy affects approximately 50 million people globally, requiring continuous monitoring for effective management. Wearable artificial intelligence (AI) technologies offer a promising solution by leveraging physiological signals and machine learning for seizure detection and prediction. However, a comprehensive synthesis of these advancements is lacking.

Objective:

This review aims to systematically explore and map the existing literature on AI-driven wearable technologies for epilepsy, identifying device characteristics, AI methodologies, biosignal measurements, validation approaches, and research gaps.

Methods:

A scoping review was conducted following the PRISMA-ScR guidelines. A systematic search was performed across six electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) to identify relevant studies published up to December 2023. Study selection and data extraction were performed independently by six reviewers. The extracted data was synthesized narratively.

Results:

A total of 68 studies met the inclusion criteria. Research in this domain has increased significantly since 2021, with India, the United States, and China leading contributions. The studies examined both commercial (45.6%) and non-commercial (47.1%) wearable devices, with Empatica smart bands being the most frequently used. The primary biosignals monitored included activity measures (54.4%), cardiovascular metrics (45.6%), brain activity (35.3%), and electrodermal activity (33.8%). Support vector machines (42.6%), random forests (22.1%), and convolutional neural networks (16.2%) were the most commonly applied AI techniques, with 77.5% of models focused on seizure detection and 22.5% on seizure prediction. Closed-source data predominated (64.7%), limiting the generalizability of findings. The most used validation methods were leave-one-out cross-validation (30.9%) and k-fold cross-validation (29.4%), while video-EEG served as the primary reference standard (42.6%). Sensitivity (80.9%) was the most frequently reported performance metric, followed by accuracy (42.6%) and specificity (36.8%). Conclusion: Wearable AI technologies show significant promise in epilepsy management, offering real-time, continuous monitoring and early seizure detection. However, challenges remain, including limited dataset accessibility, inconsistent validation methods, and the need for standardized evaluation frameworks. Future research should focus on multimodal data integration, algorithmic optimization for wearable environments, and participatory sensing.