Edited by: Arjen van Ooyen, VU University Amsterdam, Netherlands
Reviewed by: Omid Kavehei, University of Sydney, Australia; Giovanni Pellegrino, McGill University, Canada
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The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias, and computational time. In this manuscript, we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space magnetoencephalographic (MEG) data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.
Every year 2.4 million people are diagnosed with epilepsy; it has been estimated that 25% of them respond inadequately to pharmacological treatment and could therefore be potential candidates to resective surgery (
The visual detection of HFOs in multichannel long-term iEEG is a challenging task even for an expert operator and this has so far somewhat limited a more widespread use in clinical practice. This limitation has driven recent interest in developing detection algorithms (
In this manuscript, we present EPINETLAB, a multi-graphic user interface (GUI) set of Matlab functions developed in the context of the
The ability to import data from a wide range of file formats which can be easily extended to others not yet supported with purpose-developed Matlab code;
a wide range of functions for pre-processing of brain signals such as artifact rejection, independent component analysis, signal averaging, and spectral analysis including time-frequency decomposition;
the extensible and open-source nature of this platform, which is supported by a strong research team and is enriched by plugins developed in laboratories around the world.
EPINETLAB was designed to provide an easy-to-use tool to investigate the spatial and time-frequency properties of HFOs, to identify the iEEG channels with the highest HFO rate (which we will refer to as the “HFO area”) and to provide measures to support the evaluation of the spatial distribution of the HFO area with that of the SOZ identified in the presurgical workup. The toolbox is supported by detailed documentation of each step of the analysis pipeline; parameters for the analyses can be set in GUIs designed with user-friendliness in mind. Moreover, the platform allows analysis of multiple files in a single process and the implementation of a robust channel reduction methodology was designed to reduce computational load and subject-dependent errors. An addition that is not available in other tools released so far in the public domain (
The initial validation was performed on the iEEG of 12 patients (6 female, mean age ± SD: 21.25 ± 11.34 years) who underwent presurgical evaluation either at the Niguarda Hospital (NIG), Milan, Italy or at the Birmingham Women’s and Children’s Hospital (BCH) in Birmingham, United Kingdom. Patients’ information is reported in
Patients’ information.
Patient | Pathology | Institution | Implantation type | Engel class |
---|---|---|---|---|
1 | Gliosis | NIG | SEEG | Ia |
2 | Type IIa Focal cortical dysplasia | NIG | SEEG | Ia |
3 | Type IIb Focal cortical dysplasia | NIG | SEEG | II |
4 | Type IIa Focal cortical dysplasia | NIG | SEEG | Ia |
5 | Type IIa Focal cortical dysplasia | NIG | SEEG | Ia |
6 | Type IIb Focal cortical dysplasia | BCH | SEEG | Ia |
7 | Type IIa Focal cortical dysplasia | BCH | SEEG + GRID | Ia |
8 | Ganglioglioma Grade 1 | BCH | GRID | Ia |
9 | Hippocampal Sclerosis | BCH | SEEG | Ia |
10 | Meningioangiomatosis | BCH | GRID | Ia |
11 | Type Ib Focal cortical dysplasia | BCH | GRID | Ia |
12 | Pilocytic Astrocytoma | BCH | GRID | Ia |
We chose to limit the assessment of the tool to its performance in the analysis on a set of real data acquired in the context of the presurgical evaluation of patients with drug-resistant epilepsy. We therefore measured the spatial concordance between the iEEG electrodes with the highest HFO presence and the SOZ determined using the standard clinical evaluation including ictal and interictal iEEG, functional imaging when appropriate. The goodness of the latter was supported by the post-surgical outcome at 1 year. Other equally important aspects of the assessment of software such as code quality, usability, and sustainability, and of the individual but invaluable user’s experience were outside of the scope of the present manuscript, as was measuring the performance of the automated method against human visual assessment.
For NIG patients, intracerebral stereo-EEG (SEEG) was recorded from intracranial multichannel/multi-contact electrodes (DIXI Medical, 5–18 contacts; 2 mm length, 0.8 mm diameter; leads 1.5 mm apart). The number of electrodes and the sites for implantation were decided according to anatomical and clinical data collected during the non-invasive phase of the evaluation and varied between 5 and 18 contacts per intracranial electrode (maximum number of 192 recording channels). Band-pass filter of 0.016–500 Hz was used. EEG signal was acquired continuously with a Neurofax EEG-1100 system (Nihon Koden, Tokyo, Japan) and sampled at 1 kHz with 16-bit resolution. Each channel was off-line re-referenced with respect to its direct neighbor (bipolar derivations with a spatial resolution of 3.5 mm) to cancel-out effects of distant sources that spread equally to both adjacent sites through volume conduction. When appropriate for diagnostic purpose, the iEEG signal was referenced to the average signal from two electrodes identified from anatomical and neurophysiological data to be located in the white matter. Two scalp EEG channels (Fz and Cz referenced to a mastoid electrode) and chin electromyogram were recorded in addition to SEEG for sleep staging. The simultaneous video-iEEG recordings lasted between 5 and 10 days.
For BCH patients, SEEG recordings from 128 contacts were obtained using a commercial video-EEG monitoring system (System Plus, Micromed, Italy). Data were acquired with band-pass filter of 0.016–1 KHz and sampled at 2 KHz. The remaining recording parameters were the same as those used in the NIG patients.
Five patients had intracranial strips or grids implanted. In these patients, platinum-iridium alloy electrode disks (Ad-Tech Medical Racine, WI, United States), 4 mm diameter arranged in a grid (max 8 × 8 array), strip (4 × 1 or 6 × 1), and/or a combination of these were used. Electrodes were placed in the subdural space via craniotomies and/or burr hole craniotomies as appropriate.
An expert neurophysiologist reviewed iEEG recordings and annotated the beginning of the seizures, together with the SOZ, anatomically defined by all the contacts involved in the onset of each seizure.
For each patient, peri-ictal and interictal data were analyzed by one of the authors (Lucia Rita Quitadamo) blinded to the clinical information and to the results of the diagnostic work-up. Peri-ictal data consisted of two seizure episodes. An EEG segment containing 10 min before and 2 min after the electrographic onset of each seizure was extracted and was considered as the period of interest for HFOs identification. Interictal data were extracted from 12 min of iEEG signal collected during stage III NREM sleep of the second night of the monitoring period. This choice was driven by evidence suggesting that the maximum likelihood of capturing HFOs is during NREM sleep (
The theoretical approach behind the algorithm was described in a recent paper by our group (
After signal preprocessing and segmentation, discrete wavelet transform is computed on 1-s signal windows. The preliminary data showed high-specificity and sensitivity (96.03 and 81.94%, respectively) using complex Morlet transform (
EPINETLAB is implemented as a collection of routines easily accessible from the EEGLAB main bar. Four different modules of functions can be recognized, as reported in
Functional blocks in EPINETLAB. Four different blocks of functions are defined: (1) Time-frequency transform and statistical analysis. (2) Automated HFO detection and artifact identification. (3) Performance evaluation. (4) Supplementary functions.
When the EPINETLAB plugin is installed into the EEGLAB suite, a new tab (EPINETLAB (HFOs)) is created in the main EEGLAB bar menu, see
EEGLAB main bar with the EPINETLAB plugin installed.
The
Time-frequency analysis GUI. Once an EEGLAB “.set” file is loaded, different parameters can be set up: channels on which to perform the analysis, length of the segmentation window (1 s), overlap between consecutive windows (0 s), signal time epoch to analyze (all), frequency band limits (80–250 Hz), and mother wavelet (Complex Morlet). Display resolution can be reduced in case of really high number of channels and/or sampling rate.
These consist of the length of the window for signal segmentation, the overlap between consecutive windows, the signal time interval to be analyzed, the frequency band of interest, and the continuous mother wavelet used for the transform. All the wavelets available in the
Wavelet-transformed signal can be saved into a Matlab (.mat) file and displayed as shown in
Time-frequency display GUI. In the left panel, signal in the time domain is displayed. Channels can be visualized in groups smaller than the original sample (“Group View” or “List View”) and amplitudes at different time instants can be measured, by enabling a cursor with the “Measure on” function. In the right panel, time-frequency transforms corresponding to the channels on the left are displayed and power of wavelet coefficients at different time instants or frequencies can be measured.
A functionality that performs the kurtosis-based statistical analysis on wavelet coefficients described in section HFOs Detection Algorithm is available from the time-frequency GUI (see
Statistical analysis GUI. Spectral kurtosis is computed on wavelet transforms in each segmentation window and averaged over frequencies. Kurtosis distribution is fitted with a set of known distributions available in the Matlab statistical toolbox. A kurtosis threshold is set on the distribution which equals the mean of the distribution plus 3 SD. Channels are ranked according to the number of windows with kurtosis over the threshold. Windows with kurtosis distributed uniformly over all the frequencies do not contribute to the ranking. In this case, the fitted distribution is a generalized extreme value (GEV) distribution and the threshold is equal to 13. In the upper right panel of the statistic GUI, the channels are sorted according to their mean kurtosis; in this case OF8–OF9 and OF9–OF10 are the channels with the highest number of windows with kurtosis exceeding 13. The distribution of frequencies with kurtosis over threshold is also depicted.
The distribution of spectral kurtosis of wavelet coefficients for all the segmentation windows is fitted and the kurtosis threshold (
In the
High-frequency oscillation detection GUI. This GUI allows to set up the parameters for the detection of HFOs with two different methods, the Staba and wavelet-based methods. Detection can be run on all the channels or on a subset of channels, determined, for example, from the kurtosis thresholding. For the wavelet-based analysis, a twofold artifact removal option is available: the channel-based one allows to reject candidate HFOs which synchronously spread on more than
The two implemented methods can be applied on all the channels or on a subset of them selected either manually or by the kurtosis-based thresholding as described in section HFOs Detection Algorithm. If using the kurtosis-based method, the detection of HFOs can be further refined by discarding the events that are occurring synchronously on multiple channels (potentially associated to the spreading of artifactual events) or with a power uniformly distributed over all the frequencies in the band of interest (potentially associated to HFO superimposed to spike events).
Detection results can be visualized on the original signal for the Staba detector and on the wavelet coefficients for the wavelet-based detector. The GUI in
High-frequency oscillation detection display GUI. In the left panel, the results of the detection with the Staba detector are reported, while on the right panel results of the wavelet-based detection are reported. The total number of detected HFOs is reported next to each channel. The detected HFOs are indicated in the two panels with red dotted rectangles, while the duration of the event with a text string.
The analyses steps described so far can be run sequentially, unsupervised and, more importantly, concurrently on multiple data files. A GUI has been created (
Multiple-file automatic HFO detection GUI. This GUI allows to run the time-frequency analysis, kurtosis thresholding, HFO detection, and artifact rejection in an automatic way and on multiple files. Results are saved in text files which can be used for further analyses.
The results of the detection can be saved as multiple text files and used to display the HFO rates in each contact of each electrode as color-coded bars (
High-frequency oscillation rates display. HFO rates relative to each channel (bipolar, strips, or grid contacts) are visualized as colored bars. Percentages represent the number of HFO detected on the specific channel over the total number of detected HFOs. Color ranges from black, meaning HFO rate of 0%, to white, representing the maximum HFO rate. In this case, OF09–OF10 and OF08–OF09 are the channels with the highest HFO rates, 18.66 and 15.04%, respectively.
If multiple text files are associated to consecutive time epochs on which the HFOs detection was performed, the contribution of each electrode in terms of HFO rate and relative to each epoch can be visualized as histograms, see
Contribute of the most informative channels to the overall HFO rate in six consecutive epochs.
The HFO rates of all contacts are automatically saved in a text file when the figures are created. Such files are then used in the subsequent steps leading to a probabilistic identification of the HFO area.
The identification of which channels belong to the “HFO area” is available in a further GUI (
The “Max. Value” method:
The “Tukey’s fence” method: this method is typically used when outliers need to be identified in a dataset. Electrodes with an HFO rate higher than the upper Tukey’s fence (3rd quartile + 1.5 times the inter-quartile range) of all HFO rates, are selected. Channels within the HFO area can be considered data with anomalously high-HFO rates.
The “Fuzzy c-means clustering” method (
The “
The “Kernel Density Estimation (KDE)” method: a KDE (with Gaussian kernel) (
High-frequency oscillation area identification and comparison with the SOZ. Results of the detection of HFOs on all the analyzed channels and on two selected seizure episodes can be loaded, together with the SOZ identified by clinicians. Then five different algorithms (Max. Value, Tukey’s fence, fuzzy clustering,
Once the “HFO area” is identified, the software compares its spatial distribution with that of the electro-clinically defined SOZ. The performance of this comparison is listed in a table for each method in terms of true positives (TP, channels in the HFO area and in the SOZ), true negatives (TN, channels outside the HFO area and the SOZ), false positives (FP, channels in the HFO area but outside the SOZ), false negatives (FN, channels outside the HFO area but in the SOZ), sensitivity (TP/(TP + FN)), and specificity (TN/(TN + FP)), with relative confidence intervals. Results of the HFO area identification and comparison with the SOZ can be saved in text files.
Additional functionalities, not strictly related to the detection of HFOs and the identification of the SOZ, are included in the present tool:
Functions to import/export into/from EEGLAB an “.npx/.set” file into a “.set/.npx” (
Functions to import into EEGLAB a MEG “.fif” file, native file format of the Elekta® Neuromag TRIUXTM system. Such functions are based on those found in the MNE toolbox (
Functions to alphabetically order channel labels and to manipulate their label strings.
Functions to reformat original data: users can create a bipolar montage from monopolar contacts calculating the potential difference between consecutive contacts of each SEEG electrode. This addition was deemed useful by clinicians as bipolar montages are generally preferred to unipolar in clinical SEEG interpretation to avoid the potential distortion of the intracranial signal from artefactual scalp EEG data or from positioning of the reference electrode in an active location. The average reference can also be computed and added as a “virtual” channel to the original dataset to allow reformatting the data against the average reference, a solution often used in the interpretation of intracranial subdural grids data.
Functions to segment a file into
Functions to inspect the time-frequency content of an iEEG epoch. This function was inspired by an extremely useful feature of Elpho-SEEG, a Labview software which allows to evaluate frequency distribution of EEG signal over time (
Functions for the single pulse electrical stimulation analysis (
Functions to export detected HFOs in Micromed-compatible format [Micromed s.p.a, Mogliano Veneto (TV), Italy]. This utility allows users of Micromed EEG systems to superimpose detected HFOs on a Micromed EEG file. Clinicians can integrate the results of the released automated algorithm with a more familiar clinical environment.
EPINETLAB underwent a 6-month extensive beta-testing program by experts from a wide range of backgrounds (engineers, medical doctors, clinical physiologists, and EEG technicians), in order to refine the user interface and minimize program crashes.
In the 12 patients of the validation set, the detection of the HFO area with the Tukey’s fence method and the
Results of the validation of the HFO detection and HFO area identification algorithm in peri-ictal epochs, by comparison with the clinically defined SOZ, for 12 representative subjects.
Patient | Method | TP | TN | FP | FN | Sensitivity (%) | CI (%) | Specificity (%) | CI (%) |
---|---|---|---|---|---|---|---|---|---|
1 | Tukey | 3 | 86 | 3 | 3 | 50 | 11.81–88.19 | 96.63 | 90.46–99.3 |
4 | 71 | 17 | 2 | 66.67 | 22.28–95.67 | 80.68 | 70.88–88.32 | ||
2 | Tukey | 3 | 101 | 4 | 0 | 100 | 29.24–100 | 96.19 | 90.53–98.95 |
3 | 92 | 13 | 0 | 100 | 29.24–100 | 87.62 | 79.76–93.24 | ||
3 | Tukey | 5 | 75 | 4 | 0 | 100 | 47.82–100 | 94.94 | 87.54–98.6 |
5 | 78 | 1 | 0 | 100 | 47.82–100 | 98.73 | 93.15–99.97 | ||
4 | Tukey | 4 | 76 | 3 | 2 | 66.67 | 22.28–95.67 | 96.20 | 89.3–99.21 |
4 | 77 | 2 | 2 | 66.67 | 22.28–95.67 | 97.47 | 91.15–99.69 | ||
5 | Tukey | 3 | 83 | 7 | 1 | 75 | 19.41–99.37 | 92.22 | 84.63–96.82 |
3 | 89 | 1 | 1 | 75 | 19.41–99.37 | 98.89 | 93.96–99.97 | ||
6 | Tukey | 2 | 32 | 0 | 0 | 100 | 15.81–100 | 100 | 89.11–100 |
2 | 28 | 4 | 0 | 100 | 15.81–100 | 87.50 | 71.01–96.49 | ||
7 | Tukey | 2 | 17 | 1 | 1 | 66.67 | 9.43–99.16 | 94.44 | 72.71–99.86 |
3 | 15 | 2 | 0 | 100 | 29.24–100 | 88.24 | 63.56–98.54 | ||
8 | Tukey | 1 | 20 | 0 | 1 | 50 | 15.81–100 | 84.21 | 60.42–96.62 |
1 | 17 | 3 | 1 | 50 | 1.26–98.74 | 85 | 62.11–96.79 | ||
9 | Tukey | 5 | 23 | 1 | 1 | 83.33 | 35.88–99.58 | 95.83 | 78.88–99.89 |
6 | 20 | 3 | 0 | 100 | 54.07–100 | 86.96 | 66.41–97.22 | ||
10 | Tukey | 2 | 23 | 1 | 2 | 50 | 6.76–93.24 | 95.83 | 78.88–99.89 |
2 | 18 | 6 | 2 | 50 | 6.76–93.24 | 75 | 53.29–90.23 | ||
11 | Tukey | 3 | 16 | 2 | 4 | 42.86 | 9.9–81.59 | 88.89 | 65.29–98.62 |
3 | 15 | 3 | 4 | 42.86 | 9.9–81.59 | 83.33 | 58.58–96.42 | ||
12 | Tukey | 2 | 22 | 6 | 1 | 66.67 | 9.43–99.16 | 82.14 | 63.11–93.94 |
3 | 17 | 10 | 0 | 100 | 29.24–100 | 62.96 | 42.37–80.6 |
Results of the validation of the HFO detection and HFO area identification algorithm in interictal epochs, by comparison with the clinically defined SOZ, for 12 representative subjects.
Patient | Method | TP | TN | FP | FN | Sensitivity (%) | CI (%) | Specificity (%) | CI (%) |
---|---|---|---|---|---|---|---|---|---|
1 | Tukey | 3 | 57 | 5 | 3 | 50 | 11.81–88.19 | 91.94 | 82.17–97.33 |
4 | 36 | 25 | 2 | 66.67 | 22.28–95.67 | 59.02 | 45.68–71.45 | ||
2 | Tukey | 3 | 59 | 1 | 0 | 100 | 29.24–100 | 98.33 | 91.06–99.96 |
3 | 46 | 14 | 0 | 100 | 29.24–100 | 76.67 | 63.96–86.62 | ||
3 | Tukey | 2 | 54 | 0 | 3 | 40 | 5.27–85.34 | 100 | 93.4–100 |
5 | 40 | 11 | 0 | 100 | 47.82–100 | 78.43 | 64.68–88.71 | ||
4 | Tukey | 1 | 26 | 0 | 5 | 16.67 | 0.42–64.12 | 100 | 86.77–100 |
4 | 17 | 6 | 2 | 66.67 | 22.28–95.67 | 73.91 | 51.59–89.77 | ||
5 | Tukey | 3 | 44 | 2 | 1 | 75 | 19.41–99.37 | 95.65 | 85.16–99.47 |
4 | 34 | 11 | 0 | 100 | 39.76–100 | 75.56 | 60.46–87.12 | ||
6 | Tukey | 2 | 20 | 0 | 0 | 100 | 15.81–100 | 100 | 83.16–100 |
2 | 15 | 5 | 0 | 100 | 15.81–100 | 75 | 50.9–91.34 | ||
7 | Tukey | 1 | 25 | 3 | 2 | 33.33 | 0.84–90.57 | 89.29 | 71.77–97.73 |
1 | 25 | 3 | 2 | 33.33 | 0.84–90.57 | 89.29 | 71.77–97.73 | ||
8 | Tukey | 1 | 24 | 1 | 1 | 50 | 1.26–98.74 | 96 | 79.65–99.9 |
2 | 18 | 6 | 0 | 100 | 15.81–100 | 75 | 53.29–90.23 | ||
9 | Tukey | 1 | 22 | 2 | 5 | 16.67 | 0.42–64.12 | 91.67 | 73–98.97 |
2 | 14 | 9 | 4 | 33.33 | 4.33–77.72 | 60.87 | 38.54–80.29 | ||
10 | Tukey | 2 | 15 | 2 | 2 | 50 | 6.76–93.24 | 88.24 | 63.56–98.54 |
2 | 14 | 4 | 2 | 50 | 6.76–93.24 | 76.47 | 50.1–93.19 | ||
11 | Tukey | 2 | 9 | 1 | 5 | 28.57 | 3.67–70.96 | 90 | 55.5–99.75 |
6 | 4 | 2 | 1 | 85.71 | 42.13–99.64 | 66.67 | 22.28–95.67 | ||
12 | Tukey | 1 | 11 | 3 | 2 | 33.33 | 0.84–90.57 | 78.57 | 49.2–95.34 |
3 | 6 | 6 | 0 | 100 | 29.24–100 | 50 | 21.09–78.91 |
In peri-ictal epochs, Tukey’s method resulted in average sensitivity of 70.93% and average specificity of 93.12%, while
In interictal epochs, Tukey’s method resulted in average sensitivity of 49.46% and average specificity of 93.30%, while
This manuscript presents a Matlab toolbox developed with the aim of supporting researchers and clinicians in the detection of HFOs and the identification of the SOZ in iEEG/MEG data. The tool was designed as a plugin of the EEGLAB framework, a widely used, user-friendly software for the analysis of brain electromagnetic data. The plugin is released for free upon request under the limitations of the GNU GPL 3.0.
EPINETLAB was implemented as a multi-GUI set of functions to allow users not experienced in the Matlab environment to apply advanced signal processing techniques to datasets acquired during pre-surgical evaluation. The tool is based on a structured analysis pipeline and allows to pre-process data, compute time-frequency transformation of EEG signal, operate a kurtosis-based selection of the most informative channels, which was the most innovative aspect of the algorithm released by our group, detect HFO events, reject artifact events, and finally identify the “HFO area” using appropriate and robust statistical testing. Moreover, a functionality for the statistical comparison of the HFO area with the clinically defined SOZ is provided and the process is evaluated in terms of TP, FP, TN, FN, sensitivity, and specificity.
The preliminary validation of this tool on a small group of patients who were investigated with iEEG using a combination of grid/strips and SEEG and successfully operated showed good concordance between electrodes with the highest contribution of HFOs and the SOZ identified clinically. The analysis suggests that, at least in this subset of patients, peri-ictal segments of iEEG offer a better yield than those selected in the interictal state during sleep. This finding requires further validation on larger patient cohorts and this toolbox can facilitate large-scale data analysis, removing bias due to inherent subjectivity, and lack of quantitative measures associated with visual inspection of the iEEG trace.
In our opinion, the main strengths of the toolbox are:
The compatibility with the most used file formats for brain data, which favors sharing of data and the dissemination of results.
The possibility to import and analyze MEG data, which allows to compare results on the SOZ from complementary methodologies.
The GUI-oriented approach used for the software implementation, which allows also non-specialist users to easily set parameters and independently run the analysis.
The totally automated HFO detection process, which decreases unavoidable human bias in case of high-density, long term recordings.
The possibility to modify the code and extend its functionalities, adding new wavelet transforms or new algorithm for the detection, for example, as source-code is released.
The final version of the tool ready for release incorporates improvements that resulted from the feedback received during extensive beta testing by different professional groups in three departments. With modest training the tool can be used by professionals who are conversant with properties of neurophysiological signals. HFOs detection and SOZ identification are a topic of great interest at this time in epilepsy practice and research and we developed this tool hoping that it will constitute a valid support for clinicians who are currently tasked with visual analysis of iEEG.
Finally, this software in its current implementation is intended to provide only a limited 2D graphic representation of the electrodes; the contribution of each contact to the total HFO content of the analyzed signal is color-coded as seen in
A novel user-friendly and multi-GUI EEGLAB plugin is implemented for the detection of HFOs and the identification of the SOZ, according to an innovative algorithm already clinically validated and released by or group within an EU-funded project. It provides clinicians with a set of GUI-based and user-friendly functionalities that can be available to research teams working on epilepsy presurgical workup data.
The datasets analyzed in this study will be available from the corresponding author upon reasonable request. EPINETLAB is released under the GPL version 3 License. Code is available at
This study was a retrospective/secondary analysis of anonymized data obtained in the context of standard clinical practice; as such it did not require retrospective consent and was authorized by the Comitato Etico Area C, Ospedale Niguarda Ca’ Granda (14.7.2014), and the R&D Department of the Birmingham Children’s Hospital NHS Foundation Trust (ref. APGE14 14.10.2014).
LQ designed, coded, and tested EPINETLAB. EF provided MEG data and performed the analysis. RM, LdP, and NS provided iEEG data and validated results as clinical experts. SS contributed to the conceptual stage of the software development, provided iEEG data, performed the analysis, and validated results as a clinical expert. All the authors read, reviewed, and approved the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors are grateful to the Clinical teams of the participating centers (The Birmingham Children’s Hospital NHS Foundation Trust, Niguarda Hospital and Bambino Gesú Hospital) for the invaluable contribution through accurate expert feedback on the software. The authors acknowledged Micromed S.r.l., Mogliano Veneto (TV), Italy, for making the data structure available for the project and for the support in the scoping phase of the software development.