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Proceedings Paper

Implementation and evaluation of an interictal spike detector
Author(s): Peter C. Horak; Stephen Meisenhelter; Markus E. Testorf; Andrew C. Connolly; Kathryn A. Davis; Barbara C. Jobst
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Paper Abstract

The detection of epileptiform activity, such as interictal spikes, in electrical brain recordings has important clinical and research applications. Identification of interictal spikes is often carried out manually by trained neurologists. It is a time-consuming process and can exhibit variability between experts. In this work, we develop and evaluate an automated spike detector. We implement a template-matching approach and improve its accuracy on one set of recordings using a supervised machine-learning algorithm. Evaluation with two independent datasets shows the template-matching detector to perform comparably with experts and the version augmented with a classifier. In one test dataset, variations of the detection threshold partially explain discrepancies between experts. In the other, the detector demonstrates similar behavior to an existing algorithm developed with this dataset.

Paper Details

Date Published: 21 September 2015
PDF: 11 pages
Proc. SPIE 9600, Image Reconstruction from Incomplete Data VIII, 96000N (21 September 2015); doi: 10.1117/12.2189248
Show Author Affiliations
Peter C. Horak, Geisel School of Medicine (United States)
Stephen Meisenhelter, Geisel School of Medicine (United States)
Markus E. Testorf, Geisel School of Medicine (United States)
Thayer School of Engineering (United States)
Andrew C. Connolly, Geisel School of Medicine (United States)
Kathryn A. Davis, Hospital of the Univ. of Pennsylvania (United States)
Barbara C. Jobst, Geisel School of Medicine (United States)


Published in SPIE Proceedings Vol. 9600:
Image Reconstruction from Incomplete Data VIII
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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