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Journal of Healthcare Engineering
Volume 2017, Article ID 3035606, 7 pages
Research Article

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

1KACST, Riyadh, Saudi Arabia
2KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), Electrical Engineering Department, King Saud University, Riyadh, Saudi Arabia
3King Fahad Medical City, Riyadh, Saudi Arabia

Correspondence should be addressed to Turky N. Alotaiby; as.ude.tscak@ybiatot

Received 18 May 2017; Revised 6 August 2017; Accepted 13 September 2017; Published 1 October 2017

Academic Editor: Zhongwei Jiang

Copyright © 2017 Turky N. Alotaiby et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.