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

• Published in: PloS one Vol. 11; no. 6; p. e0158276
• Main Authors: Navarrete, Miguel, Alvarado-Rojas, Catalina, Le Van Quyen, Michel, Valderrama, Mario
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.06.2016; Public Library of Science (PLoS)
• Subjects: Accessibility; Algorithms; Automation; Biology and Life Sciences; Biomarkers; Biomedical engineering; Brain; Brain - physiology; Brain - physiopathology; Brain Waves; Computer and Information Sciences; Computer Simulation; Design; EEG; Electrodes; Electroencephalography; Engineering; Engineering and Technology; Epilepsy; Evoked Potentials; Graphical user interface; Hand tools; High frequencies; Humans; Identification; Medicine and Health Sciences; Methods; Neural networks; Oscillations; Physical Sciences; Physiological aspects; Reproducibility of Results; Research and Analysis Methods; Research facilities; Researchers; Sensitivity and Specificity; Signal processing; Software; Software packages; Source code; Studies; Visual stimuli; Wavelet transforms; Colombia; France
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0158276

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.