Signal quality and patient experience with wearable devices for epilepsy management.
epilepsy
patient experience
signal quality
wearable devices
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
08
01
2020
revised:
14
04
2020
accepted:
14
04
2020
pubmed:
5
6
2020
medline:
30
1
2021
entrez:
5
6
2020
Statut:
ppublish
Résumé
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.
Types de publication
Clinical Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
S25-S35Subventions
Organisme : The Epilepsy Foundation of America's Epilepsy Innovation Institute My Seizure Gauge project
Pays : International
Informations de copyright
© 2020 International League Against Epilepsy.
Références
Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 2018;17:279-88.
Cook MJ, O'Brien TJ, Berkovic SF, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12:563-71.
Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. Seizure prediction-ready for a new era. Nat Rev Neurol. 2018;14(10):618-30.
Dumanis SB, French JA, Bernard C, Worrell GA, Fureman BE. Seizure forecasting from idea to reality. Outcomes of the My Seizure Gauge epilepsy innovation institute workshop. eNeuro. 2017;4(6):ENEURO.0349-17.2017.
Karoly PJ, Goldenholz DM, Freestone DR, et al. Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study. Lancet Neurol. 2018;17:977-85.
Baud MO, Rao VR. Gauging seizure risk. Neurology. 2018;91:967-73.
Karoly PJ, Cook MJ, Maturana MI, et al. Forecasting cycles of seizure likelihood. Epilepsia. 2020;61:776-786.
Gregg NM, Nasseri M, Kremen V, et al. Circadian and multiday seizure periodicities, and seizure clusters in canine epilepsy. Brain Commun. 2020;2:fcaa008.
Beniczky S, Conradsen I, Henning O, Fabricius M, Wolf P. Automated real-time detection of tonic-clonic seizures using a wearable EMG device. Neurology. 2018;90:e428-34.
Regalia G, Onorati F, Lai M, Caborni C, Picard RW. Multimodal wrist-worn devices for seizure detection and advancing research: focus on the Empatica wristbands. Epilepsy Res. 2019;153:79-82.
Beniczky S, Ryvlin P. Standards for testing and clinical validation of seizure detection devices. Epilepsia. 2018;59:9-13.
Onorati F, Regalia G, Caborni C, et al. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia. 2017;58:1870-9.
Fürbass F, Kampusch S, Kaniusas E, et al. Automatic multimodal detection for long-term seizure documentation in epilepsy. Clin Neurophysiol. 2017;128:1466-72.
Bruno E, Biondi A, Richardson MP. Pre-ictal heart rate changes: a systematic review and meta-analysis. Seizure. 2018;55:48-56.
Simblett SK, Bruno E, Siddi S, et al. Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: a qualitative analysis. Epilepsy Behav. 2019;97:123-9.
Simblett SK, Biondi A, Bruno E, et al. Patients’ experience of wearing multimodal sensor devices intended to detect epileptic seizures: a qualitative analysis. Epilepsy Behav. 2020;102:106717.
Lee S, Shin H, Hahm C. Effective PPG sensor placement for reflected red and green light, and infrared wristband-type photoplethysmography. In: 2016 18th International Conference on Advanced Communication Technology (ICACT). Pyeongchang, South Korea: IEEE, 2016:556-8.
Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2018;4:195.
Tamura T, Maeda Y, Sekine M, Yoshida M, Yoshida M. Wearable photoplethysmographic sensors-past and present. Electronics. 2014;3:282-302.
Sinha SR, Sullivan LR, Sabau D, et al. American clinical neurophysiology society guideline 1: minimum technical requirements for performing clinical electroencephalography. Neurodiagn J. 2016;56:235-44.
de Curtis MD, Gnatkovsky V. Reevaluating the mechanisms of focal ictogenesis: the role of low-voltage fast activity. Epilepsia. 2009;50:2514-25.
Sharma A, Purwar A, Lee YD, Lee YS, Chung WY. Frequency based classification of activities using accelerometer data. In: 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Seoul, South Korea: IEEE, 2008:150-3.
Benedek M, Kaernbach C. A continuous measure of phasic electrodermal activity. J Neurosci Methods. 2010;190:80-91.
Benedek M, Kaernbach C. Decomposition of skin conductance data by means of nonnegative deconvolution. 2010. Psychophysiology. 2010;47:647-58.
Braithwaite J, Watson D, Jones R, Mickey R. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology. 2013;49:1017-34.
Dawson ME, Schell AM, Filion DL. The electrodermal system. In: Cacioppo JT, Tassinary LG, Bernston GL, eds. Handbook of Psychophysiology. 2nd ed. Cambridge, UK: Cambridge University Press; 2000:200-23.
Taylor S, Jaques N, Chen W, Fedor S, Sano A, Picard R. Automatic identification of artifacts in electrodermal activity data. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, Italy: IEEE, 2015:1934-7.
Kocielnik R, Sidorova N, Maggi FM, Ouwerkerk M, Westerink JHDM. Smart technologies for long-term stress monitoring at work. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems. Porto, Portugal: IEEE, 2013:53-8.
Misra H, Ikbal S, Bourlard H, Hermansky H. Spectral entropy based feature for robust ASR. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Montreal, QC: IEEE, 2004:I-193.
Rezek IA, Roberts SJ. Stochastic complexity measures for physiological signal analysis. IEEE Trans Biomed Eng. 1998;45:1186-91.
Bundy DT, Zellmer E, Gaona CM, et al. Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings. J Neural Eng. 2014;11:016006.
Nurse ES, John SE, Freestone DR, et al. Consistency of long-term subdural electrocorticography in humans. IEEE Trans Biomed Eng. 2018;65:344-52.
Miller KJ, Sorensen LB, Ojemann JG, den Nijs M. Power-law scaling in the brain surface electric potential. PLoS Comput Biol. 2009;5(12):e1000609.