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. 2017:2017:3035606.
doi: 10.1155/2017/3035606. Epub 2017 Oct 1.

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Turky N Alotaiby  1 , Saud R Alrshoud  1 , Saleh A Alshebeili  2 , Majed H Alhumaid  3 , Waleed M Alsabhan  1
Affiliations

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

Turky N Alotaiby et al. J Healthc Eng. 2017.

Abstract

Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

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Figures

Figure 1
Figure 1
MEG interictal spiky and nonspiky segments.
Figure 2
Figure 2
Example of tree structure formula.
Figure 3
Figure 3
Spike detection methodology overview.
Figure 4
Figure 4
Spikes' duration distribution.
Figure 5
Figure 5
Examples of GP trees of the new feature.
See this image and copyright information in PMC

References

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