A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.
Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analys...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 46; no. 3; pp. 263 - 273 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2008
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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=105747482&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105747482 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2008 vid: 46 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105747482 NLM18071771 2009889703 10.1007/s11517-007-0289-4 NLM18071771 105747482 ppf: 263 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis. aug: au: De Lucia M Fritschy J Dayan P Holder DS De Lucia, Marzia Fritschy, Juan Dayan, Peter Holder, David S affil: Medical Physics and Clinical Neurophysiology, University College London, London, UK sug: subj: Electroencephalography Methods Epilepsy Diagnosis Signal Processing, Computer Assisted Algorithms Artifacts Blinking Brain Physiopathology Data Analysis, Statistical Factor Analysis Human ab: Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 +/- 22% at a specificity of 86 +/- 7% (mean +/- SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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