title: 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.
authors.affiliation: Department of Biomedical Engineering, Universidad de los Andes, Bogotá D.C., Colombia.|Department of Electrical and Electronics Engineering, Universidad de los Andes, Bogotá D.C., Colombia.
authors.forename: Miguel
authors.identifier: http://orcid.org/0000-0002-4903-2156
authors.initials: M
authors.lastname: Navarrete
authors.affiliation: Centre de Recherche de L'Institut du Cerveau et de la Moelle Epinière (CRICM), INSERM UMRS 975-CNRS UPR640, Hôpital de la Pitié-Salpêtrière, Paris, France.|Department of Electronics Engineering. Pontificia Universidad Javeriana. Bogotá D.C., Colombia.
authors.forename: Catalina
authors.initials: C
authors.lastname: Alvarado-Rojas
authors.affiliation: Centre de Recherche de L'Institut du Cerveau et de la Moelle Epinière (CRICM), INSERM UMRS 975-CNRS UPR640, Hôpital de la Pitié-Salpêtrière, Paris, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Le Van Quyen
authors.affiliation: Department of Biomedical Engineering, Universidad de los Andes, Bogotá D.C., Colombia.
authors.forename: Mario
authors.initials: M
authors.lastname: Valderrama
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology / Q000503:physiopathology; D058256:Brain Waves; D003198:Computer Simulation; D004569:Electroencephalography; D005071:Evoked Potentials; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software