Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques.
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 18 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/29/2015
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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=109273290&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273290 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/29/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273290 109273290 109273290 10.1155/2015/986736 109273290 ppf: 1 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques. aug: au: Fergus, Paul Hignett, David Hussain, Abir Al-Jumeily, Dhiya Abdel-Aziz, Khaled affil: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK sug: subj: Electroencephalography Artificial Intelligence Seizures Diagnosis Epilepsy Complications Seizures Etiology Human Seizures Classification Descriptive Statistics Sensitivity and Specificity Male Female Child Infant Child, Preschool Adolescence Young Adult Discriminant Analysis Algorithms Factor Analysis ROC Curve Child: 6-12 years Infant: 1-23 months Child, Preschool: 2-5 years Adolescent: 13-18 years Male Female ab: The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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