Signal quality and patient experience with wearable devices for epilepsy management
Open accessAbstract
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.
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The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.
“the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or motion artifacts by analyzing the entropy of the signal spectrum.
“A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or, more importantly, from motion artifacts by analyzing the entropy of the signal spectrum.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.
“Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
Our signal quality metric is building on, expanding, and validating this research group's average values for what they describe as good, marginal, and noise data.
“Still, our signal quality metric is building on, expanding, and validating this research group’s average values for what they describe as good, marginal, and noise data.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
These kinds of artifacts can be detected by simple thresholding as well as analysis of the rate of amplitude change, similar to the artifacts seen in the temperature signal.
“These kinds of artifacts can be detected by simple thresholding as well as analysis of the rate of amplitude change9,12, similar to the artifacts seen in the temperature signal.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
There is cursory evidence that the BVP signal quality measure correctly maps periods of good and poor signal quality to estimations of the heart rate.
“While there is cursory evidence that it correctly maps periods of good and poor signal quality to estimations of the heart rate8,9”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [9]
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- Authors
- Nasseri, M., Nurse, E., Glasstetter, M., Böttcher, S., Gregg, N. M., Laks Nandakumar, A., Joseph, B., Pal Attia, T., Viana, P. F., Bruno, E., Biondi, A., Cook, M., Worrell, G. A., Schulze‐Bonhage, A., Dümpelmann, M., Freestone, D. R., Richardson, M. P., & Brinkmann, B. H.
- Affiliations
- Mayo Clinic (US)
- The University of Melbourne (AU)
- University of Freiburg (DE)
- College of Medicine University of Illinois Peoria Illinois USA
- Bioelectronics Neurophysiology and Engineering Laboratory Department of Neurology Mayo Clinic Rochester Minnesota USA
- University of Lisbon (PT)
- King's College London (GB)
- Seer Medical Melbourne Victoria Australia
- Corresponding author
- Brinkmann, Benjamin H.
- ORCID
- Venue
- Epilepsia
- Publisher
- Wiley
- Published
- 2020-06-04
- Volume
- 61
- Issue
- S1
- Pages
- S25-S35
- ISSN
- 0013-9580 (ISSN-L)
- 1528-1167
- OpenAlex
- W3033023978
- Cited by
- 90
- References
- 40
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 23 · 2023: 15 · 2022: 14 · 2021: 18 · 2020: 2
- FWCI
- 3.85
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Epilepsia (publishedVersion)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- The Epilepsy Foundation of America's Epilepsy Innovation Institute My Seizure Gauge project
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- epilepsy, wearable sensors, signal quality, patient experience, accelerometry, photoplethysmography, electrodermal activity, EEG monitoring, Empatica E4, spectral entropy, noise detection, device comfort, patient acceptability, wearable biosensors
- MeSH terms
- Wearable Electronic Devices, Adult, Aged, Epilepsy, Female, Galvanic Skin Response, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Photoplethysmography, Monitoring, Ambulatory, Young Adult, Patient Preference, Accelerometry
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