Location of Repository

A machine learning system for automated whole-brain seizure detection

By P. Fergus, A. Hussain, David Hignett, D. Al-Jumeily, Khaled Abdel-Aziz and Hani Hamdan

Abstract

Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain’s electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier

Topics: Seizure, Non-seizure, Machine learning, Classification, Electroencephalogram, Oversampling, Information technology, T58.5-58.64
Publisher: Elsevier
Year: 2016
DOI identifier: 10.1016/j.aci.2015.01.001
OAI identifier: oai:doaj.org/article:25658d60963249fdb6f6a47f9ebd6083
Journal:
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • https://doaj.org/toc/2210-8327 (external link)
  • http://www.sciencedirect.com/s... (external link)
  • https://doaj.org/article/25658... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.