Abstract:
: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.
journal_name
Biomed Res Intjournal_title
BioMed research internationalauthors
Fergus P,Hignett D,Hussain A,Al-Jumeily D,Abdel-Aziz Kdoi
10.1155/2015/986736subject
Has Abstractpub_date
2015-01-01 00:00:00pages
986736eissn
2314-6133issn
2314-6141journal_volume
2015pub_type
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journal_title:BioMed research international
pub_type: 杂志文章
doi:10.1155/2015/319589
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journal_title:BioMed research international
pub_type: 杂志文章
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pub_type: 杂志文章
doi:10.1155/2013/781618
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journal_title:BioMed research international
pub_type: 杂志文章,评审
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doi:10.1155/2014/232451
更新日期:2014-01-01 00:00:00
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journal_title:BioMed research international
pub_type: 杂志文章
doi:10.1155/2015/432645
更新日期:2015-01-01 00:00:00
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journal_title:BioMed research international
pub_type: 杂志文章
doi:10.1155/2015/214618
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pub_type: 杂志文章,评审
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pub_type: 历史文章,杂志文章
doi:10.1155/2015/850648
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pub_type: 杂志文章,评审
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journal_title:BioMed research international
pub_type: 临床试验,杂志文章
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更新日期:2015-01-01 00:00:00