Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Multimodal approach (EDA+ACC+GYR+sEMG): 98.6% precision; ACC+EDA alone: 97.2% sensitivity

• Primary Outcome: Seizure detection accuracy using various wearable sensor combinations

• Nnt Or Sensitivity: Detection sensitivity up to 97.2%, prediction sensitivity 75.6%, false alarm rate 0.53/24h

• Confidence Interval: Not reported

Clinical Application

Promising for home-based monitoring but requires further validation studies and regulatory approval before clinical implementation Potential integration with existing epilepsy management pathways through RACGP guidelines; TGA approval would be required for medical device classification; cost-effectiveness analysis needed for PBS consideration Adults and children with epilepsy experiencing GTCS, particularly those at risk of SUDEP requiring continuous monitoring

Abstract

INTRODUCTION: Generalized tonic - clonic seizures (GTCS) is a major cause of sudden unexpected death in epilepsy (SUDEP); thus, continuous monitoring is essential. Electroencephalography (EEG) is the diagnostic gold standard; however, non-EEG wearables have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. AREAS COVERED: This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, Scopus, IEEE Xplore, and PubMed databases were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria. For seizure detection, combining electrodermal activity (EDA) with motion sensors - accelerometers (ACC) and gyroscopes (GYR) - and surface electromyography (sEMG) achieved high reliability, 98.6% precision, while using ACC and EDA alone yielded superior sensitivities up to 97.2% and a lower false alarm rate (FAR) of 0.53/24 h. Regarding seizure prediction, combining EDA, blood volume pulse (BVP), ACC, and temperature showed the highest sensitivity of 75.6%. EXPERT OPINION: Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP).Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life.Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups.Advances in wearable sensors and artificial intelligence could revolutionize epilepsy management by making continuous seizure monitoring and prediction possible in daily life. Although more evidence is required before widespread clinical adoption, these technologies hold strong potential to improve safety, autonomy, and quality of life for people living with epilepsy.

References

1. 1.Wesal, Ahmed, Hesham, Farida, Gad, Mohamed A M, Abdelrahim, Mostafa, Ahmed Sayed, Nariman, Bahgat, Nour, Zaki, Nour Allah, Rehan Youssef, Aliaa. (2026). Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review. Expert Review of Medical Devices. https://doi.org/10.1080/17434440.2026.2632757