RIPPLELAB: A comprehensive application for the detection, analysis and classification of high frequency oscillations in electroencephalographic signals

Charpier, Stéphane, Navarrete, Miguel, Alvarado-Rojas, Catalina, Le Van Quyen, Michel and Valderrama, Mario 2016. RIPPLELAB: A comprehensive application for the detection, analysis and classification of high frequency oscillations in electroencephalographic signals. PLoS ONE 11 (6) , e0158276. 10.1371/journal.pone.0158276

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Abstract

High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection—as well as several options for visualization and validation of detected events—were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Publisher:	Public Library of Science
ISSN:	1932-6203
Date of First Compliant Deposit:	29 August 2018
Date of Acceptance:	13 June 2016
Last Modified:	06 Jun 2023 02:40
URI:	https://orca.cardiff.ac.uk/id/eprint/111045

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