Seizure quantification using wearable devices

Accurate assessment of seizure occurrence is central to treatment optimization, risk stratification, and management of epilepsy, yet remains largely dependent on patient-reported seizure diaries, which are frequently incomplete and unreliable. Wearable technologies have emerged as promising tools to enable objective and continuous seizure monitoring by capturing seizure-induced physiological signals. Over the past decade, substantial progress has been made in the development and clinical validation of wearable seizure detection systems, particularly for generalized convulsive seizures, where high sensitivity can be achieved, whereas detection of nonconvulsive seizures remains more limited. However, translating detection into clinically meaningful seizure quantification presents important challenges, including variability in performance across seizure types, patient populations, and individual physiological responses, as well as differences between controlled and real-world settings. This narrative review critically examines wearable technologies for seizure quantification, integrating recent clinical evidence and expert perspectives. We discuss key sensor modalities, including accelerometry, electrocardiography-derived features such as heart rate variability, multimodal biosignal integration, and emerging ultra-long-term wearable and subcutaneous electroencephalographic systems. Each modality offers specific advantages and limitations depending on seizure type and clinical context. Attention is also given to the importance of user trust, adherence, and data quality as key determinants of clinical utility. We further examine the role of wearable seizure quantification in clinical practice, including treatment monitoring, tele-epileptology, and clinical trials, as well as potential future contributions to sudden unexpected death in epilepsy risk assessment. With continued technological advances and robust real-world clinical validation, wearable systems have the potential to transform seizure monitoring and support more precise, data-driven epilepsy care.