Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments.

In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers.

Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

Mona Nasseri, Ewan Nurse, Martin Glasstetter, Sebastian Böttcher, Nicholas M. Gregg, Aiswarya Laks Nandakumar, Boney Joseph, Tal Pal Attia, Pedro F. Viana, Elisa Bruno, Andrea Biondi, Mark Cook, Gregory A. Worrell, Andreas Schulze‐Bonhage, Matthias Dümpelmann, Dean R. Freestone, Mark P. Richardson, Benjamin H. Brinkmann

Source: https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16527

Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments.