Translator Disclaimer
Paper
21 September 2015 Implementation and evaluation of an interictal spike detector
Author Affiliations +
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter C. Horak, Stephen Meisenhelter, Markus E. Testorf, Andrew C. Connolly, Kathryn A. Davis, and Barbara C. Jobst "Implementation and evaluation of an interictal spike detector", Proc. SPIE 9600, Image Reconstruction from Incomplete Data VIII, 96000N (21 September 2015); https://doi.org/10.1117/12.2189248
PROCEEDINGS
11 PAGES


SHARE
Advertisement
Advertisement
Back to Top