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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 of the implemented MNI detection method.This method is implemented according to the following two detection steps: (i) A baseline detection based on the wavelet entropy (WEn) over an entropy threshold (ThWE), and (ii) the HFO detection, which depends on the quantity of detected baseline. If the quantity of detected baseline is greater than the threshold TB, then the HFO detection is processed by selecting events with energy higher than ThCBk in each epoch (ECB). If the baseline is not enough, then an iterative process is carried out in order to find an energy threshold (ThCCk) that detects the highest quantity of putative events. Events are selected if they have a duration greater than Tw. As published by Zelmann et al. [14], the parameters by default are set as ThWE = 0.67, TB = 5s/min, ThCBk = 99.9999 percentile, ThCCk = 95 percentile, TD = 10 ms, Tw = 10 ms, ECB time = 10s, ECC time = 60s. EOI: Events of interest.
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pone.0158276.g004: Algorithm flowchart of the implemented MNI detection method.This method is implemented according to the following two detection steps: (i) A baseline detection based on the wavelet entropy (WEn) over an entropy threshold (ThWE), and (ii) the HFO detection, which depends on the quantity of detected baseline. If the quantity of detected baseline is greater than the threshold TB, then the HFO detection is processed by selecting events with energy higher than ThCBk in each epoch (ECB). If the baseline is not enough, then an iterative process is carried out in order to find an energy threshold (ThCCk) that detects the highest quantity of putative events. Events are selected if they have a duration greater than Tw. As published by Zelmann et al. [14], the parameters by default are set as ThWE = 0.67, TB = 5s/min, ThCBk = 99.9999 percentile, ThCCk = 95 percentile, TD = 10 ms, Tw = 10 ms, ECB time = 10s, ECC time = 60s. EOI: Events of interest.

Mentions: The last algorithm incorporated, the MNI detector (MNI) was developed by Zelmann et al. [14]. In this method the signal is first band-pass filtered. Then, a baseline detection procedure based on the wavelet entropy is applied [38]. For this, the signal is divided into segments of 125 ms with 50% overlap. Next, for each segment, the normalized wavelet power of the autocorrelation function is computed using the complex Morlet wavelet [39]. Subsequently, the maximum theoretical wavelet entropy from the segment is obtained for the white noise [40], and the segment is considered as a baseline interval when the minimum entropy is larger than a threshold. If a sufficient amount of baseline exists, HFO candidates are detected in accordance with the energy, defined as the moving average of the RMS amplitude of the filtered signal. Segments with energy above a threshold and lasting more than 10 ms are considered as HFOs. Similar to other methods, events located less than 10 ms apart are considered as single events. If a sufficient amount of baseline is not present in the signal, an iterative procedure is carried out where the threshold is computed for the band-passed signal. Originally, this detection methodology was implemented with 1-min segments of EEG signal. In addition to this, we included the possibility to process the data thresholds in epochs of time specified by the user. The flowchart of the MNI algorithm is presented in Fig 4.


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 MNI detection method.This method is implemented according to the following two detection steps: (i) A baseline detection based on the wavelet entropy (WEn) over an entropy threshold (ThWE), and (ii) the HFO detection, which depends on the quantity of detected baseline. If the quantity of detected baseline is greater than the threshold TB, then the HFO detection is processed by selecting events with energy higher than ThCBk in each epoch (ECB). If the baseline is not enough, then an iterative process is carried out in order to find an energy threshold (ThCCk) that detects the highest quantity of putative events. Events are selected if they have a duration greater than Tw. As published by Zelmann et al. [14], the parameters by default are set as ThWE = 0.67, TB = 5s/min, ThCBk = 99.9999 percentile, ThCCk = 95 percentile, TD = 10 ms, Tw = 10 ms, ECB time = 10s, ECC time = 60s. EOI: Events of interest.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0158276.g004: Algorithm flowchart of the implemented MNI detection method.This method is implemented according to the following two detection steps: (i) A baseline detection based on the wavelet entropy (WEn) over an entropy threshold (ThWE), and (ii) the HFO detection, which depends on the quantity of detected baseline. If the quantity of detected baseline is greater than the threshold TB, then the HFO detection is processed by selecting events with energy higher than ThCBk in each epoch (ECB). If the baseline is not enough, then an iterative process is carried out in order to find an energy threshold (ThCCk) that detects the highest quantity of putative events. Events are selected if they have a duration greater than Tw. As published by Zelmann et al. [14], the parameters by default are set as ThWE = 0.67, TB = 5s/min, ThCBk = 99.9999 percentile, ThCCk = 95 percentile, TD = 10 ms, Tw = 10 ms, ECB time = 10s, ECC time = 60s. EOI: Events of interest.
Mentions: The last algorithm incorporated, the MNI detector (MNI) was developed by Zelmann et al. [14]. In this method the signal is first band-pass filtered. Then, a baseline detection procedure based on the wavelet entropy is applied [38]. For this, the signal is divided into segments of 125 ms with 50% overlap. Next, for each segment, the normalized wavelet power of the autocorrelation function is computed using the complex Morlet wavelet [39]. Subsequently, the maximum theoretical wavelet entropy from the segment is obtained for the white noise [40], and the segment is considered as a baseline interval when the minimum entropy is larger than a threshold. If a sufficient amount of baseline exists, HFO candidates are detected in accordance with the energy, defined as the moving average of the RMS amplitude of the filtered signal. Segments with energy above a threshold and lasting more than 10 ms are considered as HFOs. Similar to other methods, events located less than 10 ms apart are considered as single events. If a sufficient amount of baseline is not present in the signal, an iterative procedure is carried out where the threshold is computed for the band-passed signal. Originally, this detection methodology was implemented with 1-min segments of EEG signal. In addition to this, we included the possibility to process the data thresholds in epochs of time specified by the user. The flowchart of the MNI algorithm is presented in Fig 4.

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