Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Paul Fergus,David Hignett,Abir Hussain,Khaled Abdel-Aziz,Dhiya Al-Jumeily

doi:10.1155/2015/986736

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.

Highlights

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is typically associated with a clinicoelectrical correlate on the electroencephalogram (EEG)
The area under the curve (AUC) results showed improvements for several of the classifiers, with 93% achieved by the K-class nearest neighbour classifier (KNNC) classifier
We found a 4% increase in sensitivities, a 3% increase in specificities, and a 2% increase in the performance of the KNNC classifier with other classifiers improving with similar increases

Summary

Introduction

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is typically associated with a clinicoelectrical correlate on the electroencephalogram (EEG). In the absence of a reliable witness account, diagnosis in the early stages of the disease can be challenging, which may delay initiation of treatment. Where there is clinical uncertainty, paraclinical evidence from the EEG can allow earlier diagnosis and treatment. EEG capture and interpretation are time consuming and costly because interpretation can currently only be performed by specialist clinicians, trained in EEG interpretation. This has led to a recent interest in automated seizure detection [1]

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