Database of paroxysmal iceeg signals

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1 POSTER 2017, PRAGUE MAY 23 1 Database of paroxysmal iceeg signals Ing. Nikol Kopecká 1 1 Dept. of Circuit Theory, Czech Technical University, Technická 2, Praha, Czech Republic kopecnik@fel.cvut.cz Abstract. The aim of our work is to study specific aspects of brain connectivity in pharmacoresistant epilepsy. For this study, we build a dedicated database of intracranial EEG signals of the patients with implanted electrodes. It should facilitate the work of the researchers, who should focus on the specific analyses and not deal with the preparation of the raw data. The beginning of the epileptic seizures was carefully localised by the neurosurgeon in the signal and the data were separated into the preictal, ictal and interictal phases. Since the definition of these phases is not unambiguous, we are planning to investigate three different options how to select them and show some results of the connectivity, that we can with the selected data get. Keywords Epilepsies, database, connectivity, iceeg, 1. Introduction Epilepsy is the most prevalent neurological disease in the human population. It is often indicated by spontaneous and repetitive seizures that generate abnormal electrophysiological activity of the patient's brain. Our study is focused on the pharmacoresistant epilepsy patients. These patients underwent the intracranial electroencephalography (iceeg) monitoring of their spontaneous brain activity to localise the epileptogenic zone (EZ). It is expected that the complete resection or disconnecting of the EZ should lead to a seizure-free outcome. The EZ is an essential part of the epileptic network. The aim of this paper is to verify the hypothesis; that the change of the connectivity within this network during the early seizure can improve the localisation of the EZ. The connectivity analysis estimates [4, 5] the causal dependencies and a linkage between two signals based on the transfer of the information [8, 9]. In comparison to the standard methods used to measure the correlation [8, 9], the connectivity is capable of separating the primary source of the activity and secondary propagated activity [6, 7]. The database described in this paper is built to allow verification of this hypothesis in a large sample of patients who underwent invasive exploration as a part of the presurgical evaluation. The process of building the database was divided into two major parts: 1) the collection of patient information and 2) the adjustment of recorded data. Patient information: The patient's information was collected in cooperation with the Motol University Hospital. All patients were asked to sign an informed consent about the use of their personal data. The patients were divided into two groups children (<18 y.o.) and adults (>18 y.o.). Moreover, every patient is at least one year after the resection. Patient data: So as the patient's information were the patient's data collected in the Motol University Hospital and all patients were forced to sign an informed consent about the use of their personal data. The data were divided into two groups noninvasive and invasive data. 1.1 State of art The majority of the patients databases used in the EEG research consist of very few items, which strongly impact the fidelity of the derived results. On the other hand, some hospitals have already gathered a huge amount of EEG signals from many patients, but in an unstructured form not suitable for direct application in the research. The first step is, therefore, identifying the beginning of the epileptic seizure with sufficient accuracy and splitting the signal into the interictal, preictal and ictal phase. Since the definition of the interictal phase is inconsistent in the literature, we have adopted data selection approach already described in the literature [2]. For the connectivity quantification, interictal recordings during the wakefulness awake, in a resting state with eyes closed for 15 minutes were selected. The recordings selected for analysis of the interictal periods was obtained at least two days after the electrode implantation and more than 4 hours from the nearest seizure.

2 2 KOPECKÁ,DATABASE OF PAROXYSMAL IEEG SIGNALS 2. About the database The databases are usually structured as the hierarchical, relational or a network. Our database structure represents the network type. This enables easy access to the important information about each patient. As is depicted in Fig. 1, a database item for each patient is divided into the data section and the information section with the detailed inputs (cf. Tab.1). This structure allows for a swift search of the desired patients set, suitable for a further investigation. Alternatively, the knowledge of the unique patient s code can be used to access all information of the specific person. The database structure is demonstrated on the example of the patient P66. The data of this patient are divided into invasive and noninvasive. The noninvasive data originate from high-density EEG, and invasive data from iceeg, were split into preictal-ictal and interictal regions and available as well.. ms before and 200 ms after the spike, shown in Fig.2. Second method [2]: Fig.2 interictal spike definition. The recording periods they used for the connectivity quantification were entirely interictal in the awake resting state with eyes closed for 15min between 9:00 am and 12:00 am. The recordings the selected for analysis of the interictal periods were at least two days after the electrode implantation and at least 4h distant from a seizure. The ictal recording was defined as the subset of brain sites involved in the generation of seizures and also showing interictal spiking, and these parts were selected for our database. 2.1 Methods Fig.1 Concept of the database structure. Data selection and preprocessing: For the preictal period analysis, our research team has the experience to utilize 5-10 min epoch before the seizure, then the ictal phase with the seizures and a few second after. This is a proven method of selection, and we used it in the database too. For analysis of interictal period, we have selected there different approach of the data selection First method [1]: The data are recorded from a patient that is at least 30 min awake, eyes-closed and in a resting state. From the interictal part, we selected epochs of 460 ms around the maximal negativity of interictal spikes (in the paper they considered only recordings with at least five spikes). We took 260 Last method [3]: The records were recorded between 5 and 20 days. Selecting 10 nonconsecutive epochs in order to avoid the inclusion of obvious epileptic transient in the analysis. These criteria are limiting the length of the epochs of each segment to 1 s. More specifically, selected interictal consisted of 10 consecutive non-overlapping epochs of 1 s duration. 1. Results The content of the database will be illustrated on the example of the patient P66 (cf. Tab. 1). This patient was a female with the diagnosis of refractory frontal lobe epilepsy (FLE). Her epileptic seizures started when she was 13 years old and since them she was on antiepileptic drugs. However, the seizures were not completely controlled. Before the segmental resection, she was implanted with seven intracerebral electrodes in the left frontal lobe patient as is shown in Fig.3.

3 POSTER 2017, PRAGUE MAY 23 3 Tab.2 Information about the patient. was based on selecting 2s window, successive moving over investigated temporal region where the connectivity was evaluated for few discrete frequencies, while the second method has evaluated connectivity for time point before and after seizure for a large range of frequencies from 0 to 250Hz. In the Fig.4 we have performed such evaluation using the first method. The large change in the connectivity can be observed between EZ and the rest of the brain, where a significant drop in the connectivity just a few seconds before the seizure has occurred. The connectivity decreases to about half of the original value at all frequencies. In the case of the connectivity from the rest of the brain to the EZ, no changes in connectivity were measured. Finally, within the EZ is the biggest change in the range between 40-90Hz. Fig.4 Temporal evolution of the connectivity evaluated between EZ and outer regions for five frequencies Fig.3 The implanted electrodes, where R is the reference, x are the defect electrode contacts. We will present here our methodology applied to investigate a change of the connectivity during the epileptic seizure of the patient. At first, the beginning of the seizure was identified from the spectrogram and with a help of the neurologist were identified the channels which could correspond to the EZ. Further, the connectivity was evaluated using two different methods. The first of them In order to investigate the influence the changes of the connectivity as a function of the of frequency, the connectivity in frequency bands (0.5-4, 4-8, 8-12, 12-25,25-50,...with 50Hz steps) was calculated. Moreover, the connectivity in each band was evaluated with a 1Hz step to increase the frequency resolution as is shown in Fig.5 and Fig.6. In addition, the surrogate data were evaluated, i.e. data with the randomised phase of the harmonic components. The connectivity of these surrogate data should be lowest possible and it measures a "random leak" of connectivity due to a noise. It gives us a lower threshold of sensitivity of this method. In the Fig. 5 we calculated the connectivity in several frequency bands for the ictal period. The teeth at 50, 100, 150,... Hz are due to a notch filter of the 50Hz brum signal and its higher harmonics. One can see, that the connectivity is increasing with a frequency and within each frequency range is the value almost constant. The difference between the measured connectivity and surrogated connectivity is sometimes even slightly negative, which is a consequence of the random error in the measured connectivity possible

4 4 KOPECKÁ,DATABASE OF PAROXYSMAL IEEG SIGNALS issues with a short window length at the lowest frequencies. The connectivity in the preictal phase is depicted in Fig.6. Also, in this case, a negative difference between measured and surrogated connectivity can be observed at lowest frequencies. Additionally, the residuum is too small; within the uncertainty is the connectivity almost a zero. Although the connectivity before the subtraction of the surrogates is at least 2x higher, then in ictal phase, after the subtraction the difference between both phases has almost vanished. 3. Conclusions The extensive and detailed database is an essential part of the epileptic research and its building is important, but not always very exciting part of our research. The signals available in this database will serve for the testing of the various methods to identify reliably the EZ zone. As an example, we have introduced so connectivity measure. We have shown, that even if the connectivity has significantly decreased, the connectivity with subtracted a random leak component estimated from a surrogate data is changed only marginally. Acknowledgements This work has been supported by grants from the Ministry of Health of the Czech Republic (AZV CR A) and the Czech Science Foundation ( S). Access to CESNET storage facilities provided under the programme Extension of the National R&D Information Infrastructure in Regions (eiger), CZ.1.05/3.2.00/ , part of the Operational Program Research and Development for Innovations, and Large Infrastructure CESNET (LM ), part of the Projects of Large Infrastructure for Research, Development, and Innovations, is acknowledged. The data and clinical results was provided by Motol University Hospital, Department of Neurology and Department of Paediatric Neurology. Fig.5 Frequency profiles of the ictal connectivity. References [1] COITO, A., PLOMP, G., GENETTI, M., ABELA, E., WIEST, R., SEECK, M., VULLIEMOZ, S. (2015). Dynamic directed interictal connectivity in left and right temporal lobe epilepsy. Epilepsia, 56(2), [2] BETTUS, G., RANJEVA, J. P., WENDLING, F., BÉNAR, C. G., CONFORT-GOUNY, S., RÉGIS, J., GUYE, M. (2011). Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations. PLoS ONE, 6(5). [3] VAROTTO, G., TASSI, L., FRANCESCHETTI, S., SPREAFICO, R., & PANZICA, F. (2012). Epileptogenic networks of type II focal cortical dysplasia: A stereo-eeg study. NeuroImage, 61(3), [4] KOPECKÁ, Nikol. Funkční organizace epileptogenní zóny [5] KOPECKÁ, Nikol. Functional organization of epileptogenic zone. POSTER 2016, 20 th International Student Conference on Electrical Engineering. [6] JANČA, Radek, Analýza invazivních EEG signálů v epileptologii Ph.D. Thesis Fig.6 Frequency profiles of the preictal connectivity. [7] KORZENIEWSKA, Anna, et al. Determination of information flow direction among brain structures by a modified directed transfer function (ddtf) method. Journal of neuroscience methods, 2003, 125.1: [8] KORZENIEWSKA, Anna, et al. Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG. NeuroImage, 2014, 101:

5 POSTER 2017, PRAGUE MAY 23 5 [9] DIESSEN, Eric, et al. Functional and structural brain networks in epilepsy: what have we learned?. Epilepsia, 2013, 54.11: Nikol KOPECKÁ received the Master s degree in Biomedical Engineering from FEE at Czech technical university in Prague, Czech Republic in She is currently working towards the Doctoral degree Electrical Engineering Theory in the research group ISARG and SAMI. Her current research interests include intracranial electroencephalography (iceeg), connectivity analysis and epilepsy diagnostics.