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

Navarrete M, Alvarado-Rojas C, Le Van Quyen M, Valderrama M - PLoS ONE (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Universidad de los Andes, Bogotá D.C., Colombia.

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.

No MeSH data available.


Related in: MedlinePlus

Structure proposed for HFO sharing.Channel_n names correspond to electrode labels of channels where putative electrodes were detected.
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pone.0158276.g011: Structure proposed for HFO sharing.Channel_n names correspond to electrode labels of channels where putative electrodes were detected.

Mentions: A main feature of RIPPLELAB is that the result of the analyses carried out during a session can be saved in files for future reviews, validations or sharing. For this purpose, RIPPLELAB proposes a custom-made structure MAT-File with the extension.mat modified to.rhfe which is created after each analysis. The general organization of this structure is described in Fig 11. Finally, logs of all operations accomplished during the detection procedure can be saved, which includes general information about the HFO detection method.


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

Navarrete M, Alvarado-Rojas C, Le Van Quyen M, Valderrama M - PLoS ONE (2016)

Structure proposed for HFO sharing.Channel_n names correspond to electrode labels of channels where putative electrodes were detected.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920418&req=5

pone.0158276.g011: Structure proposed for HFO sharing.Channel_n names correspond to electrode labels of channels where putative electrodes were detected.
Mentions: A main feature of RIPPLELAB is that the result of the analyses carried out during a session can be saved in files for future reviews, validations or sharing. For this purpose, RIPPLELAB proposes a custom-made structure MAT-File with the extension.mat modified to.rhfe which is created after each analysis. The general organization of this structure is described in Fig 11. Finally, logs of all operations accomplished during the detection procedure can be saved, which includes general information about the HFO detection method.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Universidad de los Andes, Bogotá D.C., Colombia.

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.

No MeSH data available.


Related in: MedlinePlus