Abstract
Epilepsy affects 3.4 million people in the United States, and despite availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impacts quality of life. High frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last two decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time-consuming and can still produce variable results due to their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed and accuracy. Here, we present HFOApp: a free, open source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multi-channel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify and characterize HFOs within electrophysiological data sets.
SIGNIFICANCE
We introduce a MATLAB-based graphical user interface designed to facilitate visual marking of high frequency oscillations in electrophysiological data by prioritizing usability, speed, and accuracy. It allows clinicians and researchers to quickly and easily visualize and mark multiple channels of raw and band-pass-filtered data simultaneously, either in the same window or in separate windows, facilitating fast and accurate discrimination between real high frequency oscillations and spike artifacts. The implementation of both automatic detection and interactive automation-assist options significantly speeds up visual marking of high frequency oscillations. The simple data structure used by the toolbox also increases its ease of use. These features make it a useful toolbox that is valuable in the field.
Footnotes
GZ, TN, AS, QY, KG, MT, JWT, GL no competing interests declared; CZ reviewing editor, eNeuro.
This work is supported by The National Institute on Deafness and Other Communication Disorders (NIDCD) grants R01-DC-018539 (Christina Zelano) and R01-DC-016364 (Christina Zelano).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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