Limits...
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

Algorithm flowchart of the implemented HIL detection method.The epoch analysis is included in order to evaluate long-term recordings computing the envelope threshold (Thk) with local variations of amplitude. As proposed by Crèpon et al. [12], the parameters by default are: Thk = 5-SD and Tw = 10 ms. Epoch (Epk) Time = 3600s. EOI: Events of interest.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4920418&req=5

pone.0158276.g003: Algorithm flowchart of the implemented HIL detection method.The epoch analysis is included in order to evaluate long-term recordings computing the envelope threshold (Thk) with local variations of amplitude. As proposed by Crèpon et al. [12], the parameters by default are: Thk = 5-SD and Tw = 10 ms. Epoch (Epk) Time = 3600s. EOI: Events of interest.

Mentions: The third method we included was proposed by Crépon et al. [12]. In this method, the signal is first filtered between a selected frequency range, and the envelope is then computed with the Hilbert transform. For an event to be considered valid, two conditions must be met: first, for each event, the local maximum must exceed a threshold of 5 SD of the envelope calculated originally over the entire recording or from a time interval. Second, each detected HFO must have a minimal time length of 10 ms. This method is called Hilbert Detector (HIL) in RIPPLELAB, and its flowchart is presented in Fig 3. As in the STE detector case, we included the possibility to analyze the threshold by epochs specified by the user.


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)

Algorithm flowchart of the implemented HIL detection method.The epoch analysis is included in order to evaluate long-term recordings computing the envelope threshold (Thk) with local variations of amplitude. As proposed by Crèpon et al. [12], the parameters by default are: Thk = 5-SD and Tw = 10 ms. Epoch (Epk) Time = 3600s. EOI: Events of interest.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0158276.g003: Algorithm flowchart of the implemented HIL detection method.The epoch analysis is included in order to evaluate long-term recordings computing the envelope threshold (Thk) with local variations of amplitude. As proposed by Crèpon et al. [12], the parameters by default are: Thk = 5-SD and Tw = 10 ms. Epoch (Epk) Time = 3600s. EOI: Events of interest.
Mentions: The third method we included was proposed by Crépon et al. [12]. In this method, the signal is first filtered between a selected frequency range, and the envelope is then computed with the Hilbert transform. For an event to be considered valid, two conditions must be met: first, for each event, the local maximum must exceed a threshold of 5 SD of the envelope calculated originally over the entire recording or from a time interval. Second, each detected HFO must have a minimal time length of 10 ms. This method is called Hilbert Detector (HIL) in RIPPLELAB, and its flowchart is presented in Fig 3. As in the STE detector case, we included the possibility to analyze the threshold by epochs specified by the user.

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