Fig 1.
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.
Fig 2.
Algorithm flowchart for the implemented SLL detection method.
In Gardner et al. [13] the parameter Thk is set as the 97.5 percentile of the energy epoch (Epk); we set as default Tw = 12 ms to include events larger than 6 oscillations at 500Hz. Epoch (Epk) Time = 180s. EOI: Events of interest.
Fig 3.
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.
Fig 4.
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.
Fig 5.
RIPPLELAB multi-window approach.
The user interface is constructed as an intuitive tool for HFO analysis. A typical analysis can be carried out as follows: (a) In the Select Electrodes window, the user selects the files and electrodes to analyze. The user has the option to display the electrodes selecting the Display Ch Button or just to save the selection without plotting electrodes with the Apply button. (b) If bipolar or average montages are needed, the Assembly Electrodes window gives options for this purpose. (c) If the user chooses to draw the electrodes, controls for handling the display are available in the main GUI. Here, the user has the possibility to inspect further information from the displayed signals with tools such as filters, frequency spectrum, time-frequency plots and time cursors. (d) In the HFO–Detection Methods window the parameters for HFO detection are set and the detection method is launched. (e) In the HFO Analysis Tool window, the detected events are validated. Several options have been included in order to provide different criteria for the validation of real HFOs.
Fig 6.
RIPPLELAB flowchart for data analysis.
Visual pre-processing and feature extraction from displayed electrodes is an optional step but permits a better selection criterion before launching the automatic HFO detection. Red dashed lines connect blocks when electrodes are displayed.
Fig 7.
Features in RIPPLELAB main GUI.
An intuitive environment for navigation has been developed, where the user can easily manipulate different options for the display of electrodes. Several options for signal analysis are supplied. These options give users the possibility to make better decisions on the selection of detection parameters. Filter and Time-Frequency panels can be shown or hidden as needed through the display control panel (Panels Display Controls box). Post visualization allows an easy navigation between detected events by presenting red vertical lines at each detected position.
Fig 8.
The visual marking panel allows the user the easy identification of HFOs through the visualization of time-frequency plot, filtered and PS of the displayed interval for a selected electrode. For the segment surrounded by cursors, the following measures are provided: PS, fast ripple index, ripple index and gamma index, including the maximum frequency for each band. Options for automatic detection of HFO events
Fig 9.
Besides selecting electrodes for detection, the user can select different parameters for the execution of the detection method. By default, these values correspond to those published in the respective original works. (a.) Visual marking. (b.) Short Time Energy Detector parameters. (c.) Short Line Length Detector parameters. (d.) Hilbert Detector parameters. (e.) MNI Detector parameters.
Fig 10.
HFO Analysis Tool window for validation of detected HFOs.
The electrode can be selected in General Information section. Navigation and deleting along detected events can be done in Validation Controls. Frequency Controls and Window Controls allow users to manage the visualization of axes displayed and Power Spectrum Density. Event Controls gives the possibility to re-locate the detected even or classify it as gamma, ripple, fast ripple or other type.
Fig 11.
Structure proposed for HFO sharing.
Channel_n names correspond to electrode labels of channels where putative electrodes were detected.
Fig 12.
Simulated events over normalized real iEEG background.
(A) Gamma, (B) Ripple, (C) Fast Ripple and (D) Spike + Fast Ripple events. TOP: Raw signal with the superimposed simulated event. MIDDLE: Filtered signal in the 80–500 Hz frequency band with the detected event for each method in red. BOTTOM: Time-Frequency plot for the raw signal segment. STE Det. (STE detection), SLL Det. (SLL detection), HIL Det. (HIL detection), MNI Det. (MNI detection).
Table 1.
Performance comparison between STE and SLL methods over a large database.
Fig 13.
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.
Table 2.
Event classification for each HFO detection method after visual validation of selected segments.