Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study.
Purpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate tr...
| Publicado en: | Seizure Vol. 65; pp. 48 - 55 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134689038&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134689038 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10591311 O00 jtl: Seizure issn: 10591311 maglogo: N pubinfo: dt: Feb2019 vid: 65 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 134689038 134689038 NLM30611010 134689038 10.1016/j.seizure.2018.12.024 NLM30611010 134689038 ppf: 48 ppct: 7 formats: tig: atl: Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study. aug: au: Johansson, Dongni Ohlsson, Fredrik Krýsl, David Rydenhag, Bertil Czarnecki, Madeleine Gustafsson, Niclas Wipenmyr, Jan McKelvey, Tomas Malmgren, Kristina affil: Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden sug: subj: Electroencephalography Methods Accelerometry Methods Seizures Diagnosis Aged Adult Magnetic Resonance Imaging Seizures Algorithms Female Adolescence Middle Age Male Young Adult Videorecording False Positive Results Human Aged: 65+ years Adult: 19-44 years Adolescent: 13-18 years Middle Aged: 45-64 years Female Male ab: Purpose: The aim of this prospective, video-electroencephalography (video-EEG) controlled study was to evaluate the performance of an accelerometry-based wearable system to detect tonic-clonic seizures (TCSs) and to investigate the accuracy of different seizure detection algorithms using separate training and test data sets.Methods: Seventy-five epilepsy surgery candidates undergoing video-EEG monitoring were included. The patients wore one three-axis accelerometer on each wrist during video-EEG. The accelerometer data was band-pass filtered and reduced using a movement threshold and mapped to a time-frequency feature space representation. Algorithms based on standard binary classifiers combined with a TCS specific event detection layer were developed and trained using the training set. Their performance was evaluated in terms of sensitivity and false positive (FP) rate using the test set.Results: Thirty-seven available TCSs in 11 patients were recorded and the data was divided into disjoint training (27 TCSs, three patients) and test (10 TCSs, eight patients) data sets. The classification algorithms evaluated were K-nearest-neighbors (KNN), random forest (RF) and a linear kernel support vector machine (SVM). For the TCSs detection performance of the three algorithms in the test set, the highest sensitivity was obtained for KNN (100% sensitivity, 0.05 FP/h) and the lowest FP rate was obtained for RF (90% sensitivity, 0.01 FP/h).Conclusions: The low FP rate enhances the clinical utility of the detection system for long-term reliable seizure monitoring. It also allows a possible implementation of an automated TCS detection in free-living environment, which could contribute to ascertain seizure frequency and thereby better seizure management. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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