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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

Typical events found during the evaluation procedure are presented over a 300 ms window.The filter and time frequency scalogram has been established in the frequency band from 60 Hz to 500 Hz. Examples of (a) correct HFO detections and (b) false detections are displayed. As shown in the HFO Analysis Tool interface, figures in rows (A) correspond to raw data from detected events, filters of such events are presented in row (B) and time frequency plots in row (C). For detected events, figure (a1) shows a gamma event, (a2) a ripple and (a3) a fast ripple. For false detections, figure (b1) presents a detected spike, (b2) an artifact and (b3) an example of background noise.
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pone.0158276.g013: Typical events found during the evaluation procedure are presented over a 300 ms window.The filter and time frequency scalogram has been established in the frequency band from 60 Hz to 500 Hz. Examples of (a) correct HFO detections and (b) false detections are displayed. As shown in the HFO Analysis Tool interface, figures in rows (A) correspond to raw data from detected events, filters of such events are presented in row (B) and time frequency plots in row (C). For detected events, figure (a1) shows a gamma event, (a2) a ripple and (a3) a fast ripple. For false detections, figure (b1) presents a detected spike, (b2) an artifact and (b3) an example of background noise.

Mentions: For the second part of the analysis, a total of 14.804 events were visually reviewed for an expert. For all methods, 4542 events were classified as valid HFOs, 5115 as spikes, and 4967 as others. In our study, the MNI detector presented the highest number of HFO detections (n = 1929), but also had the highest level of false detections (true HFOs events represented only 27% of total detections). Conversely, the STE detector presented the lowest number of HFOs detected events but with the highest true detection rate (n = 418 corresponding to 43% of total detections). Moreover, the HIL method obtained the highest spike detection rate (50%) and SLL the highest noise detection rate (49%). For a complete summary of the results see Table 2. Examples of different types of detected events are present in Fig 13.


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)

Typical events found during the evaluation procedure are presented over a 300 ms window.The filter and time frequency scalogram has been established in the frequency band from 60 Hz to 500 Hz. Examples of (a) correct HFO detections and (b) false detections are displayed. As shown in the HFO Analysis Tool interface, figures in rows (A) correspond to raw data from detected events, filters of such events are presented in row (B) and time frequency plots in row (C). For detected events, figure (a1) shows a gamma event, (a2) a ripple and (a3) a fast ripple. For false detections, figure (b1) presents a detected spike, (b2) an artifact and (b3) an example of background noise.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4920418&req=5

pone.0158276.g013: Typical events found during the evaluation procedure are presented over a 300 ms window.The filter and time frequency scalogram has been established in the frequency band from 60 Hz to 500 Hz. Examples of (a) correct HFO detections and (b) false detections are displayed. As shown in the HFO Analysis Tool interface, figures in rows (A) correspond to raw data from detected events, filters of such events are presented in row (B) and time frequency plots in row (C). For detected events, figure (a1) shows a gamma event, (a2) a ripple and (a3) a fast ripple. For false detections, figure (b1) presents a detected spike, (b2) an artifact and (b3) an example of background noise.
Mentions: For the second part of the analysis, a total of 14.804 events were visually reviewed for an expert. For all methods, 4542 events were classified as valid HFOs, 5115 as spikes, and 4967 as others. In our study, the MNI detector presented the highest number of HFO detections (n = 1929), but also had the highest level of false detections (true HFOs events represented only 27% of total detections). Conversely, the STE detector presented the lowest number of HFOs detected events but with the highest true detection rate (n = 418 corresponding to 43% of total detections). Moreover, the HIL method obtained the highest spike detection rate (50%) and SLL the highest noise detection rate (49%). For a complete summary of the results see Table 2. Examples of different types of detected events are present in Fig 13.

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