Show simple item record

dc.contributor.authorOnorati, Francesco
dc.contributor.authorRegalia, Giulia
dc.contributor.authorCaborni, Chiara
dc.contributor.authorLaFrance, W Curt
dc.contributor.authorBlum, Andrew S
dc.contributor.authorBidwell, Jonathan
dc.contributor.authorDe Liso, Paola
dc.contributor.authorEl Atrache, Rima
dc.contributor.authorLoddenkemper, Tobias
dc.contributor.authorMohammadpour-Touserkani, Fatemeh
dc.contributor.authorSarkis, Rani A
dc.contributor.authorFriedman, Daniel
dc.contributor.authorJeschke, Jay
dc.contributor.authorPicard, Rosalind W.
dc.date.accessioned2022-11-22T19:16:15Z
dc.date.available2022-11-22T19:16:15Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/146599
dc.description.abstract<jats:p><jats:bold>Background:</jats:bold> Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).</jats:p><jats:p><jats:bold>Methods:</jats:bold> Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).</jats:p><jats:p><jats:bold>Results:</jats:bold> Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (<jats:italic>p</jats:italic> &amp;gt; 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (<jats:italic>p</jats:italic> &amp;lt; 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (<jats:italic>p</jats:italic> &amp;lt; 0.001).</jats:p><jats:p><jats:bold>Conclusions:</jats:bold> Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.</jats:p>en_US
dc.language.isoen
dc.publisherFrontiers Media SAen_US
dc.relation.isversionof10.3389/FNEUR.2021.724904en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceFrontiersen_US
dc.titleProspective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Uniten_US
dc.typeArticleen_US
dc.identifier.citationOnorati, Francesco, Regalia, Giulia, Caborni, Chiara, LaFrance, W Curt, Blum, Andrew S et al. 2021. "Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit." Frontiers in Neurology, 12.
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.relation.journalFrontiers in Neurologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-11-22T19:09:39Z
dspace.orderedauthorsOnorati, F; Regalia, G; Caborni, C; LaFrance, WC; Blum, AS; Bidwell, J; De Liso, P; El Atrache, R; Loddenkemper, T; Mohammadpour-Touserkani, F; Sarkis, RA; Friedman, D; Jeschke, J; Picard, Ren_US
dspace.date.submission2022-11-22T19:09:41Z
mit.journal.volume12en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record