Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques

Fergus, P, Hignett, D, Hussain, A, Al-Jumeily, D and Abdel-Aziz, K (2015) Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques. BIOMED RESEARCH INTERNATIONAL, 2015. pp. 1-17. ISSN 2314-6133

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques..pdf - Published Version
Available under License Creative Commons Attribution.

Download (856kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Science & Technology; Life Sciences & Biomedicine; Biotechnology & Applied Microbiology; Medicine, Research & Experimental; Research & Experimental Medicine; SUPPORT VECTOR MACHINES; NEURAL-NETWORKS; SIGNAL CLASSIFICATION; APPROXIMATE ENTROPY; PREDICTION METHODS; UTERINE ELECTROMYOGRAPHY; NONLINEAR FEATURES; WAVELET TRANSFORM; SPIKE DETECTION; DECISION TREE
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Computer Science & Mathematics
Related URLs:
Date Deposited: 24 Feb 2016 10:18
Last Modified: 04 Sep 2021 14:34
DOI or ID number: 10.1155/2015/986736
URI: https://researchonline.ljmu.ac.uk/id/eprint/703
View Item View Item