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Signal quality and patient experience with wearable devices for epilepsy management
Author(s) -
Nasseri Mona,
Nurse Ewan,
Glasstetter Martin,
Böttcher Sebastian,
Gregg Nicholas M.,
Laks Nandakumar Aiswarya,
Joseph Boney,
Pal Attia Tal,
Viana Pedro F.,
Bruno Elisa,
Biondi Andrea,
Cook Mark,
Worrell Gregory A.,
SchulzeBonhage Andreas,
Dümpelmann Matthias,
Freestone Dean R.,
Richardson Mark P.,
Brinkmann Benjamin H.
Publication year - 2020
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.16527
Subject(s) - photoplethysmogram , electroencephalography , wearable computer , computer science , usability , data quality , epilepsy , noise (video) , signal (programming language) , medicine , artificial intelligence , pattern recognition (psychology) , computer vision , embedded system , human–computer interaction , engineering , metric (unit) , programming language , operations management , filter (signal processing) , psychiatry , image (mathematics)
Abstract 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. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. 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.