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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

Algorithm flowchart for the implemented STE detection method.The epoch analysis is included in order to analyze long-term recordings computing the energy threshold (Thk) with local energy. In Staba et al. [11] the parameters are set as follows: Thk = 5-SD, TD = 10ms, Tw = 6ms and ThB = 3-SD. Epoch (Epk) Time = 600s. EOI: Events of interest.
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pone.0158276.g001: Algorithm flowchart for the implemented STE detection method.The epoch analysis is included in order to analyze long-term recordings computing the energy threshold (Thk) with local energy. In Staba et al. [11] the parameters are set as follows: Thk = 5-SD, TD = 10ms, Tw = 6ms and ThB = 3-SD. Epoch (Epk) Time = 600s. EOI: Events of interest.

Mentions: The first implemented method, Short Time Energy (STE) is the algorithm proposed by Staba et al. [11]. In brief, the wideband EEG signal is band-pass filtered in the high frequency range. The energy from the filtered signal is then computed using the RMS defined by the equationE(t)=1N∑k=t−N+1ix2(k)(1)within a N = 3 ms window, and successive RMS values greater than 5 standard deviations (SD) above the overall RMS mean are selected as putative HFO events if they last more than 6 ms. Finally, only the events containing more than 6 peaks greater than 3 SD above the mean value of the rectified band-pass signal are retained. In addition, the events separated by 10 ms or less are marked as a single oscillation. In the original paper, the estimation of the energy threshold depended on the complete analyzed segment, which had a duration of 10-min. In RIPPLELAB, the energy threshold can be computed for the entire signal, as originally proposed by Staba et al. [11], or for shorter segments, as suggested by Gardner et al. [13]. The flowchart of the implemented STE algorithm is shown in Fig 1.


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 for the implemented STE detection method.The epoch analysis is included in order to analyze long-term recordings computing the energy threshold (Thk) with local energy. In Staba et al. [11] the parameters are set as follows: Thk = 5-SD, TD = 10ms, Tw = 6ms and ThB = 3-SD. Epoch (Epk) Time = 600s. EOI: Events of interest.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0158276.g001: Algorithm flowchart for the implemented STE detection method.The epoch analysis is included in order to analyze long-term recordings computing the energy threshold (Thk) with local energy. In Staba et al. [11] the parameters are set as follows: Thk = 5-SD, TD = 10ms, Tw = 6ms and ThB = 3-SD. Epoch (Epk) Time = 600s. EOI: Events of interest.
Mentions: The first implemented method, Short Time Energy (STE) is the algorithm proposed by Staba et al. [11]. In brief, the wideband EEG signal is band-pass filtered in the high frequency range. The energy from the filtered signal is then computed using the RMS defined by the equationE(t)=1N∑k=t−N+1ix2(k)(1)within a N = 3 ms window, and successive RMS values greater than 5 standard deviations (SD) above the overall RMS mean are selected as putative HFO events if they last more than 6 ms. Finally, only the events containing more than 6 peaks greater than 3 SD above the mean value of the rectified band-pass signal are retained. In addition, the events separated by 10 ms or less are marked as a single oscillation. In the original paper, the estimation of the energy threshold depended on the complete analyzed segment, which had a duration of 10-min. In RIPPLELAB, the energy threshold can be computed for the entire signal, as originally proposed by Staba et al. [11], or for shorter segments, as suggested by Gardner et al. [13]. The flowchart of the implemented STE algorithm is shown in Fig 1.

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