Optimal features for online seizure detection.
This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative...
| Published in: | Medical & Biological Engineering & Computing Vol. 50; no. 7; pp. 659 - 670 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Jul2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104468666&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104468666 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2012 vid: 50 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104468666 NLM22476713 2011597980 10.1007/s11517-012-0904-x NLM22476713 104468666 ppf: 659 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Optimal features for online seizure detection. aug: au: Logesparan L Casson AJ Rodriguez-Villegas E Logesparan, Lojini Casson, Alexander J Rodriguez-Villegas, Esther affil: Electrical and Electronic Engineering Department, Imperial College London, London, UK sug: subj: Diagnosis, Computer Assisted Methods Epilepsy Diagnosis Algorithms Electroencephalography Methods Human Sensitivity and Specificity Signal Processing, Computer Assisted ab: This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity-specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5-25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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