title: Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.
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.
authors.affiliation: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
authors.forename: Paul
authors.identifier: 0000-0002-7070-4447
authors.initials: P
authors.lastname: Fergus
authors.affiliation: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
authors.forename: David
authors.identifier: 0000-0001-6851-4508
authors.initials: D
authors.lastname: Hignett
authors.affiliation: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
authors.forename: Abir
authors.identifier: 0000-0001-8413-0045
authors.initials: A
authors.lastname: Hussain
authors.affiliation: Applied Computing Research Group, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
authors.forename: Dhiya
authors.identifier: 0000-0002-9170-0568
authors.initials: D
authors.lastname: Al-Jumeily
authors.affiliation: The Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley, Liverpool L9 7LJ, UK.
authors.forename: Khaled
authors.identifier: 0000-0001-6168-4838
authors.initials: K
authors.lastname: Abdel-Aziz
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007223:Infant; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012535:Scalp; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult