DEVELOPEMENT OF MATLAB-BASED GRAPHICAL USER INTERFACE (GUI) FOR DETECTION OF HIGH FREQUENCY OSCILLATIONS (HFOs) IN EPILEPTIC

PATIENTS

SahbiChaibi1’3; RomainBouet1,2; Julien Jung1,2; TarekLajnef3; MounirSamet1; Olivier Bertrand1

Abdennaceur Kachouri3,4; KarimJerbi1

1Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, University Lyon1, Brain Dynamics and Cognition Team, Lyon, F-69000, France. 2 HospicesCivils of Lyon, Neurological Hospital, Functional Neurology and EpileptologyDept, Lyon, F-69003, France. 3Sfax University, National engineering school of Sfax, LETI Laboratory, ENIS BPW 3038- Sfax, Tunisia. 4 Gabes University, ISSIG: Higher Institute of Industrial Systems, Gabes CP 6011, Tunisia.

ABSTRACT High-Frequency Oscillations (HFOs) in the 80-500 Hz band are important biomarkers of epileptogenic brain areas and could have a central role in theprocess of epileptogenesis and seizure genesis. Visual marking of HFOs is highly time consuming and tedious especially for long electroencephalographic (EEG) recordings. Automated HFO detection methods are potentially more efficient, repeatable and objective.Therefore,numerous automatic HFOs detection methodshave been developed. Toevaluate and compare theperformance of thesealgorithms in an intuitive and user-friendly framework accessible to researchers, neurologists and students,it is useful to implement the various methodsusing adedicated Graphical User Interfaces (GUI). In this paper we describe a GUI-based tool thatcontains three HFOs detectionmethods. It allowsthe user to test and runthree different methods based respectively on FIR filter, Complex MORLET Wavelet andmatching pursuit (MP). We also show how the GUI can be used to measure the performanceof each method. Generally,high sensitivity entrains high false-positive detection rates. For that, the developed GUI contains a supplementary module that allowsan expert(e.g. neurologist) to reject false detected events and only save the clinically relevant (true) events. In addition, the GUI presented here can be used to perform classification, as well as estimation of duration, frequency and position of different events. The presented software is easy to use and can easily be extended to include further methods. We thus expect it to become a valuable clinical tool for diagnosis of epilepsy and research purposes.

Index Terms— Epilepsy, High frequency oscillations (HFOs), Stereo- electroencephalographic (SEEG), Graphical User Interface (GUI).

1. INTRODUCTION Over the past decades, the research into high frequency bands of EEG [1]greater than 80 Hz was restricted due to high computational power demands and to hardware limitations inherent to EEG systems (e.g. sampling rate, hardware filters). However, with the advent of new technology, the increase in computational power, the decrease in the cost of digital storage has facilitated research into higher frequency bands of EEG such as the study of High Frequency Oscillations (HFOs) in Epileptic Patients. High Frequency Oscillations have been reported in brain activity recorded in epileptic rat models and in patients with epilepsy [2-3-4]. HFOs have been divided into Ripples 80-250 Hz and Fast Ripples 250-500Hz [1-2]. It has been suggested that Ripples are a signature of both normal [1] and pathologic [5] brain processes, whereas Fast Ripples have been primarily identified in epileptogenic brain tissue in both patients and animal models of epilepsy [2-6]. HFOs can be recorded using intracereberalElectroencephalography [7] (iEEG) in epileptic patients with partial epilepsy (surgical candidates).Fast Ripples are considered to be a specific marker of the epileptogenic brain areas and their detection helpsneurologists localize the seizure onset zone (SOZ) with high accuracy. The rate of occurrence[2-5-6], the power[6] and the duration[5-8]of Fast Ripples are significantly higher inside rather than outside the SOZ. Visual marking of long hours of interictal intracranial electroencephalography (iEEG) recordings is a tedious and time-consuming task [1-9]. It could

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take approximately 10 hours to visually mark HFOs in a 10-channels data set of 10 min recordings [9]. Therefore automated markings are required because they are faster than the manual marking of HFOs. Therefore, numerous automatic HFOs detection methods have been developed; For example; one method [2] used a finite impulse filter (FIR) and reported a sensitivity of 84%. A second study [1] also used FIR filter and indicated a sensitivity of 75.9% and a false discovery rate (FDR) of 10.6% at the optimal trade-off. In the same study a MORLET wavelet[1] was used to detect HFOs, it achieved a sensitivity of 70.8% and an FDR= 13.1% at the optimal compromise. Another study based on Matching Pursuit was used to detect Ripples [10] but performance was not reported for this study. It is generally practicalto represent the output of such algorithmsusing a Graphical User Interface (GUI) for several raisons; first, a commonanalysistask can be performed by many users.Second; we can share our program(s) with other membersof our work group;third GUIs allow for our algorithms to be embedded in a user-friendly environment. Finally; we can build an interactive demonstration to easily demonstrate and share the concept or idea of an algorithmwithcolleagues and clinicians. In this paper we describe a Graphical User Interface (GUI) that contains three HFOdetection methodsbased respectively on FIR filter, CMOR wavelet and the matching pursuit. It allows the user to select and run anyone of them after adapting the required parameters.We used this GUI to calculate the performance measure for the threemethods on a sample iEEG data set. In addition, the presented GUI allows us to reject false detected events, in addition to classifying and estimating the durations, frequencies and positions of relevant events. The developed GUI-based tool might become a valuable clinical tool for the estimation of the SOZ in epilepsy and for research purposes such as the automatic or semi-automatic determination of Ripples and Fast Ripples rates.

2. METHODS AND DATA

2.1. Intracranial EEG Data The data used here contains 24-channels Stereotactic Electroencephalograpgy (SEEG)of34 sec recordings in epileptic patient with focal epilepsy. The SEEG data were recorded during interictalnon rapid eye movement (NREM) sleep periods. An experienced reviewer identified sleep stages 3 and 4 during NREM sleep because the occurrence of HFOs in this sleep-phase is thought to be higher than that during periods of wakefulness and REM sleep [11]. The clinical data set was acquired at the MNI (Montreal Neurological Institute, Canada), it was band-pass filtered at 0.1-500 Hz and sampled at 2,000 Hz. The sampled data was then quantized using a 16 bit analog-to-digital converter. 2.2. Detection Methods The proposed GUI is comprised of fourmajor modules: - Detection of HFOs based on FIR filter. - Detection of HFOs based on complex MORLET wavelet. - Detection of HFOs based on Matching Pursuit.

- Rejection of false detections and classification of relevant events. 2.2.1. HFOs detection basedon FIR filter This method was previously developed in the study by Staba et al. [2]. Each wideband SEEG channel is digitally band-pass filtered between Fmin=80Hz and Fmax=450Hz using a linear finite impulse response filter. Then, a running RMS (Root Mean Square) signal is computed from the band-pass data as shown in equation 1.

(1)

Successive RMS values that exceed the RMS threshold and last longer than a duration threshold DT= (1/Fmax)*C were detected as a putative HFO and delimited by onset and offset boundary time markers. Researchers assume that an HFO event has only one characteristic frequency so that regular wave-cycle is visible in HFO. The parameter C wasset to a certain number of wave-cycles which can be 3,4,5[2,6,12]. Consecutive events separated by duration less 10 ms were combined as one HFO event. Marked events are then subject to an additional criterion to be detected as candidate HFOs that they must have at least 2*C peaks greater than the threshold of rectified band- pass filtered signal above 0 μv. The RMS threshold was computed as a function of the mean of the RMS signal + 5 standard deviation and the rectified band- pass signal threshold was computed as a function of the mean of rectified filtered signal + 3 standard deviation using the entire EEG data (also containing HFOs) and it’s filtered version. This may result in a high reference value and low sensitivity. In our study we improved that by computing the two thresholds only from SEEG segments not containing HFOs (baselines segments) based on time invariant mode [4]. In our study we found:RMS threshold= *0.962, Rectified band pass signal threshold= *0.808, where is a regulation parameter of reference level. 2.2.2. HFOs detection based on Complex MORLET wavelet This method was developed in the study [1,12]. To Compute the power coefficients X(f, t), a complex MORLET wavelet ψ(t) was used,which is defined as follows:

(2)

Where is the characteristic frequency of the mother wavelet . The standard deviation of the Gaussian window is set to σ 1. The wavelet family was chosen such that the ratio of center frequency to bandwidth is equal to

σ= 7.

Whereσ = 1/2π σ . The mother wavelet defined above, can be scaled by a factor a in frequency axe and translated by an amount b in time, then is known as a daughter wavelet which is defined as follows:

(3)

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X(f,t) are computed from wavelet power by transforming scale a into pseudo-frequency f and replacing bwith time t. Pseudo frequency values of80:5:450 Hz are used to sample the band of interest.X(f, t) represents a three-dimensional surface described in time (x-axis), pseudo-frequency (y-axis), and coefficient (z-axis) in dB. If an HFO is present, it will create a local maximum in X(f,t). For each local maximum, the frequency f’ is selected such that the height of the surface X(f, t) is greatest at f’. For X(f’, t)> K(f’), where K represents the power threshold which is a functionof baselines segments[1] as shown in section2.2.1,the parameter allows to vary the threshold level Let [t’,t’’] delimit the portion of the surface X(f’,t) above K(f’). If the temporal width t’’-t’ exceeds DT (f’) which is defined in equation 4, then the segment corresponding to this temporal width is detected as candidateHFO. Note thatwith c as the number of specified wave-cycles, DT is defined as:

(4)

2.2.3. HFOs detection based on matching pursuit Matching Pursuit (MP) has beenpreviosuly used to detect Ripples[10]. The construction of a big dictionnary for MP decomposition using Matlab sotware is very time-consuming. Therfore; to achieve our task, we used a fast implementation of the MP decomposition available at http://eeg.pl/mp [13]in conjunction with our developed M-files. MP decompostion[14] consists of three steps: -First step: searching the best-match atoms :The search for the best-match atoms is performed iteratively within big redundant Dictionary D of Gabor atoms rich enough to fit all the structures possibly occurring in the signal of interest. The dictionary is constructed from a normalized real Gabor atom

defined in equation-5, where is the frequency of the sinusoid (Hz) is used to quantify the frequency of the HFO in this work.u (ms) is the position in time corresponding to the peak of the gaussian envelope which is the center of the atom used to quantify the central timing of an HFO train. The scale sapproximatesthe width of the Gaussian, it used to quantify the duration of a HFO train.The phase in rad corresponding to the phase term of the sinusoid.

At the ith (i = 1, 2, . . .) iteration, a best-match atom is selected from dictionary D, which maximizes the correlation with the residual. The procedure can be described by:

Where is the original signal. -Second step: residual after matching At the ith iteration, the weighted best-match atom,

is derived from through:

(7) The next residual, can be obtained by subtracting

from the previous residual , as shown in equation 8. is the original signal iEEG.

- Third step: stopping criterion: Stop the procedure if

Where P is the energetic percentage of reconstructed signal which can be defined as a combination of M weighted Gabor atoms. This criterion was chosen so that the HFOs components especially Fast Ripples have low amplitudes could be well extracted. If the stopping criterion is not satisfied at the ith iteration, the (i+1) iteration will start from step (2) with a residual ; otherwise, the procedure stops. At this time, the decomposition of signal can be described as follows:

After that; each extracted Gabor atom can be considered as candidate HFO if it has afrequency between 80Hz and 450Hz and its scale exceeds a duration threshold . The parameters of this method are the number of waves cycles c and the energetic parameter P. Note:the parameter Pcan be chosen through the DOS window of MP4 [13]decomposition usingthe instruction: Set –e value.value can be chosen between 0 and 100. 2.2.4. Rejection of false detections and classification of relevant events The majority of HFOsdetection studies have reported a high false detection rates [1-15-16]when a high sensitivity is achieved. These false detectionsare arising essentially from the filtering of spikes [17], sharp waves, transients without HFOs and backgrounds. Therefore, a visual control of different events is indispensabletask [15-18] after detection; it must be performed by an experienced neurologist who sorts only the true or relevant oscillations. After that; the classification into ripples and fast ripples, the computing of the duration, position and frequencyof different relevant events can be performed with our GUI. That can give us useful information about HFOs behavior and epilepsy mechanism. 2.3. Visual marking and Performance measure 2.3.1 Visual marking To visually mark HFOs events. The reviewer viewed a raw of SEEG and a band pass-filtered version at 80-450 Hz simultaneously [4]. The filtered SEEG was viewed at a higher gain than unfiltered SEEG. The filter removes lower frequency components and helps to locate HFOs. The higher gain is necessary because HFOs have very low amplitudes

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compared to the unfiltered SEEG. The reviewer classified such SEEG segment as HFO segment if at least one oscillation with at least specified n cycles in the frequency band [80-450] Hz was present. HFOs can be classified into two sub-types: Independent HFO and spike with HFO. The result of the review process is considered as ground-truth to calculate the detection performance. 2.3.2. Performance -The Sensitivity is defined as the proportion of Positives (HFOs as classified by the reviewer) that were detected automatically as candidate HFOs. is defined as follows:

Pos: are different HFOs segments visually identified. Dpos (Detected Positives): is the number of pos which overlap with at least one candidate-HFO. - False discovery rate (FDR) is defined as the proportion of probable HFOs overlapping with the Negatives (background as classified by the reviewer). is defined as follows:

Where TP (True Positives): is the number of candidate-HFOs which overlap with at least one Pos.FP (False Positives): is the number of candidate-HFOs which did not overlap with any pos event. The Sensitivity and the FDR values usually rangefrom 0 and 100%. The actual visual markings and processing of signals were performed with custom codes which were implemented with MATLAB (Mathworks, software version 7.5) and EEGLAB software.

Note: Performance measure in terms of Receiver Operating Characteristic (ROC) curves is normally calculated using sensitivity and specificity [1]. However, in our case, the performance measure is calculated using FDR instead of specificity. This is due to the fact that HFO detection is an example of rare-event detection. Therefore, the probability of a classified EEG segment being a positive is much smaller than the segment being a negative.This means that the overall duration of HFOs (positives) is much smaller than that of the background (negatives). The false discovery rate does not depend on the duration or the number of negatives and is therefore not affected by the disparity between positives and negatives in rare-event detection scenarios. Therefore, we chose FDRto calculate the performance measure.

3. RESULT AND DISCUSION

Snapshots of our developed GUI are illustrated respectivelyin figure.2/3/4 respectively for FIR, wavelet and MP method.Figure.2 illustratesthe GUI module of detection of

HFOs using FIR filter.Pushing on FIR button then Run button allows us to run the algorithm and display of detected HFOs. Figure.3 illustrates the GUI module of detection of HFOs using CMOR wavelet.Pushing on wavelet button, then Run button allows running the algorithm and display of detected HFOs. Figure.4 illustrates the GUI module of detection of HFOs using MP. To choose this method, it’s enough to push MPbutton. Run button allows such user to run the algorithm based on MP and to display of detected HFOs. To test the performance of different methods, a recording of 15 seconds rich enough by HFOs, spikes without HFOs and backgrounds was used. Two experienced reviewers (RB and JJ) visually identified different HFOs in this segment; the first one marked 16 events and the second one marked 18 events, then the onset and end time position of different visualized HFOs were saved in databases to exploit them later in performance measure. Figure.1 shows the sensitivity vs. FDR for the three methods; the number of wave cycles was fixed to 3, whereas the varying inputparameter is correspond to for FIR and wavelet methods and to the method based on MP The result in figure.1 is represented in form of the mean value between the two reviewers. As illustrated in figure.1; both sensitivity and false discovery rate increase and decrease simultaneously as a parameter value is varied for different methods. So that, HFOs detectionalgorithms to be clinically acceptedor used for research goals, they must be sufficiently sensitive and the false discovery rate is acceptably low. However, as illustrated in figure.1, high sensitivity (exceeds 95%) entrains high false detection rateswhich arise essentially from the filtering of spikes, sharp waves, transients without HFOs and backgrounds.Our detection results are comparable to previous studies which also reported high FDR [1-16-15-18]. Thereforewe addeda supplementary moduleto the GUI in which all detectedcandidate events can be individually visualized and evaluated by an experienced neurologist as illustrated in figure 5. (A/C). The dedicated GUI allows the neurologist to reject false detection and only savethe clinically relevantevents.After that a classification into ripples and fast ripples can be done. This step is done manually for this time-domain method based on FIR (figure.5.B), however the classification is done automatically for the two other methods as illustrated in figure.5.D.Finally all relevant events are saved in the database to exploit them later in diagnosis and research goals such as the determination of rate [5]-[6] or the ratio of Fast Ripples/ Ripples [2] in each channel. Moreover, the developed GUI allows clinicians to analyze the duration, the frequency and latency of the relevant events. All these criteria can be critical in helping the neurologists identify the SOZ[5]-[9]. We therefore expect the proposed GUI to be an additional valuable tool for clinical investigations of epilepsy. Future work involves evaluating interrater concordance, i.e. the reliability of the expert manual against which the automatic methods were tested. Another potential interesting path that could be further explored is to address the question of whether a consensus automatic procedure that combines all three available methods (e.g. letting each method vote on each event) could outperform the results of a single approach.

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Fig 1.Sensitivity vs. false discovery rate for different methods.

The number of wave cycles is fixed at c=3 and the varying inputparameters correspond to for FIR and wavelet methods,

and to forthe method based on MP.

4. CONCLUSION The expert reviewer typically spent a long time reviewing the EEG dataset taking about 10 hours to visually mark HFOs in a 10-channels data set of 10 min recordings. Compared to this, the multiplemethods of HFOs detection included in our GUIonly require a short time to perform the HFO detection operation. CMOR wavelet is considered the best for detection of HFOs because it is sensitive to detect HFOs with a minimum FDR compared to FIR and MP methods. The dedicated GUI proposed here also allows the user to reject falsedetection events via visual inspection of the results of the automatic classification. To date, the majority of HFOs detection methodsare frequency based decomposition that is easily lead to high rate of false-positive detections which arise essentially from the filtering of spikes and sharp waves without HFOs. Further studies based on advanced signals processing such as the morphological,statistical and geometric analysis are needed to improvethese results. That is what our research work will focus on in the near future.

ACKNOWLEDGMENT We wish to thank the Montreal Neurological Institute (Canada) for kindly providing sample data which we used to test and compare the performance of the methods we implemented in the proposed GUI.

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Fig2. Detection of HFOs using FIR.

Fig3. Detection of HFOs using CMOR wavelet (Fmin=80 Hz/ Fmax=450Hz).

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Fig4. Detection of HFOs using Matching pursuit with P=99.95%.

Fig5. Examples of rejection of false detections and classification of relevantevents. (A/C)-Rejection of false events with delete buttons (B)-manual classification of events based on FFT for FIR method (D)-

Automatic classification of events for Wavelet and MP methods.

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