RIPPLELAB: A Comprehensive Application for the Detection, Analysis and Classification of High Frequency Oscillations in Electroencephalographic Signals

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.

Extrahippocampal high‐frequency oscillations during epileptogenesis

“…The selected data were later imported to the EEGLAB for preprocessing. HFOs (100‐500 Hz) were detected based on the method suggested by Staba et al and performed in a MATLAB (MathWorks, Natick, MA, USA) toolbox RIPPLELAB (see Appendix for details). The HFO data that were within the same group were combined into larger files, and the HFO events were detected based on onset of the oscillations.…”

How to record high‐frequency oscillations in epilepsy: A practical guideline

“…First, the application of the filter results in oscillatory patterns not only for oscillations buried in the low-frequency background but also for all kinds of sharp transients (spikes, artifacts). 71 It contains a wide range of tools for visual and automatic HFO annotation and validation. 14,67 Time-frequency transformations form a starting point for feature extraction (power or power ratios, separation of high-frequency activity from lower frequency activity by a trough, identification of blobs, or computer vision).…”

“…68,69 RIPPLELAB is a MATLAB toolbox that is available to the research community (https:// github.com/BSP-Uniandes/RIPPLELAB) under terms of the GNU General Public License. 71 It contains a wide range of tools for visual and automatic HFO annotation and validation. The standard EEG software programs are starting to offer HFO-reviewing toolboxes in which automatic detectors may eventually be integrated.…”

Gamma‐aminobutyric acidergic transmission underlies interictal epileptogenicity in pediatric focal cortical dysplasia

“…28 Briefly, after filtering (80-500 Hz) and timefrequency analysis with the wavelet transform, HFOs were either visually detected or automatically detected using Hilbert method. 28 Briefly, after filtering (80-500 Hz) and timefrequency analysis with the wavelet transform, HFOs were either visually detected or automatically detected using Hilbert method.…”

“…High-frequency oscillations (HFOs) were analyzed with Ripple Lab according to previously described methodology. 28 Briefly, after filtering (80-500 Hz) and timefrequency analysis with the wavelet transform, HFOs were either visually detected or automatically detected using Hilbert method. Duration and mean frequency of the HFO events were then noted for IIDs and preictal discharges (PIDs), respectively.…”