Spatial and Temporal Analysis of Interictal Activity in the Epileptic Brain

Transcription

1 Spatial and Temporal Analysis of Interictal Activity in the Epileptic Brain Paul McCall, Mercedes Cabrerizo, Malek Adjouadi Florida International University Department of ECE Miami, FL, USA {pmcca, cabreriz, Abstract In clinical and research settings numerous algorithms have been developed for the analysis of electroencephalographic (EEG) data in the diagnosis of epilepsy and throughout the process of localizing the seizure onset zone (SOZ). This research highlights the merging of two major detection algorithms for the identification of High-Frequency Oscillations (HFO), also known as ripples, and Interictal Spikes (IIS) in both the spatial and temporal domain. IIS and HFO have become recognized as reliable biomarkers in identification of the epileptic focus in the cortex of epileptic patients. While IIS, commonly referred to as spikes, have classically been used for the diagnosis of epilepsy and for source localization techniques, ripples have been associated with more generalized cortex regions of interest and surrogate biomarkers for the seizure onset zone (SOZ). This research aims to combine these two analyses by correlating HFO and IIS events in the time domain for each electrode recording with the goal of yielding a more complete understanding of the abnormal electrical activity of the cortex in epileptic patients. Results are offered in the form HFO and IIS events per electrode as well as a surface representation of the number of temporally superimposed HFO and IIS per electrode. Keywords EEG processing, High frequency oscillation, Interictal spike I. INTRODUCTION According to the National Institute of Neurological Disorders and Stroke (NINDS), more than two million people in the United States, and over million people around the world, have been diagnosed with epilepsy or have experienced a seizure. Epilepsy is a neurological disorder that is characterized by a predisposition to unprovoked recurrent seizures. Seizures are brought about by a burst of abnormal electrical activity in the brain. This abnormal activity is a manifestation of the hypersynchronous discharge of a population of cortical neurons []. Epilepsy is a complex neurological disorder and there exist numerous clinical techniques and modalities for diagnosis. Computed tomography (CT), electroencephalogram (EEG), magnetic resonance imaging (MRI), Magnetoencephalography (MEG), positron emission tomography (PET), and functional MRI (fmri), are used throughout the diagnosis process. Although the researcher and the doctor are equipped with many non-invasive imaging and recording techniques, these techniques may not yield the highest resolution or certainty in the localization of the epileptogenic region. In terms of epileptic patients, resection surgery is the last option. The hope of resection surgery is to remove the portion of the cortex that is responsible for the abnormal activity such as seizures. In cases when the patient is experiencing seizures due to lesions on the cortex, MRI scans as well as other modalities can fairly accurately and efficiently locate the tumor for resection. In these cases resection surgery is highly effective. However there exist cases where the characteristics of the epilepsy event are more difficult to define, these are cases where there is no apparent physical abnormality in the cortex of the brain. Therefore many times the aformentioned clinical techniques do not provide an adequate amount of data on which to draw conclusions for possible resection surgery. It is at this point that further analysis and thorough imaging and registration across multiple modalities is necessary if the resection surgery is an option and is to have a positive outcome. An invasive monitoring technique known as subdural EEG or electrocorticogram (ECoG) may be performed by placing subdural electrode grids on the cortex of the brain in a prolonged (~ week) monitoring session []. During this monitoring, seizures may be captured, as well as high frequency oscillations (HFO), and interictal spikes (IIS). During ECoG recording, it is routine clinical practice to place multiple electrode arrays on different areas of interest to the neurologists. Multiple arrays act to both eliminate certain cortical regions of interest and designate cortical areas where more analysis may be needed. For example, it is customary for a patient undergoing ECoG to have sixty-five or more implanted electrodes []. Both IIS and HFO have become recognized as reliable biomarkers in identification of the epileptic focus of the brain [,,]. Section II outlines the methodology implemented in this research. The results are demonstrated in Section III of this paper. Finally, conclusion and future work are laid out in Section IV. II. METHODOLOGY This section describes the algorithms that will be implemented, as well as their importance to neuroscience and epilepsy research. Continuous slow wave sleep (CSWS) subdural EEG was recorded and analyzed for seven epilepsy patients. Patients

2 were recorded via the placement of electrode arrays on the cortex of the brain. The size and placement of the electrode arrays was dependent upon other pre-surgical evaluations including scalp EEG, fmri, and transcranial magnetic stimulation (TMS) techniques. For this study the number of electrodes per patient varies between electrodes, which are comprised of a grid of x and x electrodes respectively. Each patient is monitored at length and a minute segment of CSWS EEG data is chosen by neurologists for analysis. Sampling rates for clinical EEG recordings are set at khz. A. HFO Detection Significant contributions have been made towards epilepsy research with the discovery of high-frequency oscillations in the epileptic brain within the last few decades. This is in part due to the technological advances in clinical EEG recordings. With the incorporation of various brain imaging techniques, sampling rates eclipsing khz, as well as smaller and smaller electrode arrays, the localization and visualization of brain electrical activity is becoming more and more available to patients at the clinical level. High-frequency activity is usually defined as cortical activity (> Hz). The suggestion that population neuronal activity at high frequencies may be involved in epileptogensis and seizure genesis led to increased interest in this phenomenon in experimental and clinical epileptology []. Clinical EEG recordings are recorded for several hours at sampling rates of khz and above for HFO detection. HFO can be classified as ripples occurring in the Hz range and fast ripples occurring in the Hz range []. This research will analyze ripples in the Hz spectral band. The implementation of this algorithm was previously validated by neurologists at Miami Children s Hospital via comparison with known results, with all electrode signals of interest being identified. The HFO algorithm implemented in this research is outlined below. Data is filtered to remove DC offset and isolate spectral band of interest, Hz. Normalization is performed across all electrodes. Threshold operator is determined by the statistical parameters of certain electrode(s). Ripple detection loop is run on entirety of recordings o Recordings are windowed with overlapping windows. o o Threshold crossings are counted. Ripple/HFO hits are detected as a function of the number of threshold crossings for each set of windowed data. Results are output in the form of ripples per electrode recordings, with a corresponding time stamp. B. IIS Detection Ictal states are defined as durations in which the patient is experiencing a seizure. Interictal activity or recordings take place between these ictal states, or seizures. IIS are short, sharp transients that are observed in the EEG and MEG of epilepsy patients within the ictal period between seizures []. IIS are of major clinical significance when it comes to the diagnosis of epilepsy. IIS detection and analysis assist doctors in the pre-surgical evaluation of patients. Seventy percent of people who suffer from epilepsy are properly treated with medication and their seizures and other adverse effects of epilepsy are controlled. While this is a large majority of the epileptic population, roughly one third of epileptics search for alternative routes for treatment. One alternative may be resection surgery. In the study of IIS, it is believed that spikes emanate from the source of the seizure, the epileptic focus, and propagate to the cortex, or surface of the brain. Spikes therefore have been indicative of the epileptic focus, which is of major significance when resection surgery is considered. Source localization is performed by analyzing interictal spikes and calculating the inverse problem using a brain compartment model and sophisticated mathematical algorithms that produces a dipole source []. The algorithm implemented for the detection of IIS is an adaptation of the algorithm presented in [9] and is based on the orthogonal Walsh operator. This algorithm implementation has been shown to produce results with a precision (positive predictive value) of 9% and sensitivity of %. This process is outlined below. Data is down-sampled from khz to Hz sampling rate and DC offset is removed. Data is filtered using a th order IIR Butterworth bandpass filter isolating the spectral band of interest, Hz. th order notch filter is implemented to remove Hz additive noise. st and nd order Walsh transforms are calculated having differing lengths of,, and. The st and nd order Walsh Aggregate is constructed from the summation of Walsh transforms of differing lengths. Signal peaks are detected and counted; statistical properties of peaks are utilized for the definition of a dynamic threshold. Walsh aggregates are then subjected to derived threshold to remove portions of the signal which do not meet the threshold requirement. Remaining peaks are tested versus ten criteria in order to be characterized as a detected IIS. Results are output in the form of IIS per electrode recordings with the time stamp/sample point of each detected IIS. C. Spatial and Temporal Analysis of HFO and IIS The output of the aforementioned HFO and IIS detection algorithms is in terms of IIS or HFO per electrode with the time stamp for each recorded spike or ripple. At this point, the analysis is concerned with visualization, correlation, and possible superposition of these two seemingly independent events in time and space. A novel algorithm is developed for the merging of these two detection algorithms in the hopes discovering their relationship with one another and if possible

3 their combined relationship to cortex regions or electrodes of interest. It has been shown previously that HFO and IIS occur with greater proportion in seizure-onset areas when compared to electrode recording outside the seizure onset area []. This research aims to build on this finding by not only examining the raw counts of IIS and HFOs, but also to examine the number of these events that occur simultaneously or within a close proximity to one another in the temporal domain. The process of this analysis is outlined below and demonstrated in Fig.. Figure. Method Overview Results are taken from the IIS and HFO detection algorithms in terms of detections per electrode and the corresponding time stamp for each detection. Time stamps for each IIS are compared against each HFO time stamp for each electrode. If an HFO occurs within a specified duration of a HFO, that electrode is recorded as having a HFO- IIS event. A surface is constructed representing the number of HFO-IIS events. III. RESULTS Figures are shown below representing the analysis of four patients. Patient had a total of -electrodes implanted on the cortex, of which are analyzed in Fig.. Patient had electrodes implanted with electrodes subject to analysis. Patient had electrodes implanted with electrodes subject to analysis. Patient had electrodes implanted with electrodes subject to analysis. Electrode arrays of smaller than x electrodes were not subject to this analysis. The figures are laid out in such a way that top subplot in the first column represents the number of HFO per electrode, the subplot in the middle of the first column represent the number of IIS per electrode, and the bottom subplot in the first column represents the number of events in which a HFO and IIS happened within msec of each other. The second column illustrates the same data using isolines to give the reader a -D visualization of the activity. Isolines approximate and connect the regions in which similar levels of activity occur. In Fig. and Fig. areas of high activity can be seen in both the HFO plots as well as the IIS plots. Fig. shows a highly centralized area of activity in the middle of the upper region of the HFO surface, while the IIS surface shows a few areas of activity that are less localized and therefore distributed throughout the surface. The HFO-IIS superimposed surface in Fig. illustrate two areas of high activity, one of which corresponds with the HFO surface while the other area corresponds loosely with the IIS surface. The HFO-IIS surface seems to smooth out and erode areas of the electrode grid while highlighting other areas, analogous to an increase in contrast. Fig. shows similar results to Fig.. In Fig. both the HFO surface and the IIS surface locate and highlight areas of high activity with the IIS surface showing a more generalized distribution of activity. The HFO-IIS surface in Fig. appears to identify areas of high activity in both the HFO surface as well as the IIS surface while filtering out other areas. Fig. illustrates the ability of the analysis to erode the HFO-IIS surface when compared to the HFO and IIS surface independently. While both the HFO surface and IIS surface in Fig. show scattered areas of low activity, the HFO-IIS surface seems to ignore most of the this activity and concentrate on a particular quadrant of the electrode array that does not stand out as an area of high-activity in either HFO or IIS surface. Both the HFO and IIS surfaces in Fig. show areas of high activity along the bottom row of electrodes, although the electrodes identified in the bottom row of the HFO surface are not the same electrodes identified in the bottom row of the IIS surface. The HFO-IIS surface in Fig. identifies an area of high activity that corresponds with one area found in the HFO plot, while ignoring the other area of high activity in the HFO plot and also ignoring both areas of high activity in the IIS plot. The HFO-IIS surface in Fig. also identifies another area of high activity along the rightmost column of electrodes that was not highlighted on either of the two previous HFO or IIS surfaces.

4 HFO Per Electrode Figure. Patient ( Electrode Array) HFO Per Electrode Figure. Patient ( Electrode Array)

5 HFO Per Electrode Figure. Patient ( Electrode Array) HFO Per Electrode Figure. Patient ( Electrode Array)

6 IV. CONCLUSIONS & FUTURE WORK Recent advances in epilepsy research have focused on HFO and high-frequency activity as possible and reliable biomarkers for the seizure onset zone. Spike detection techniques have been the premier diagnosis tool for epilepsy as well as for source localization. This research aims to merge these two powerful analysis tools in order to analyze their relationship with electrodes and cortex regions of interest. Fig. shows quantitatively the results of such an analysis. It can be seen from the figures that the merging of these two analyses tends to enhance the contrast between regions. This process is manifest as morphological erosion in the HFO- IIS surface. Certain areas of the HFO-IIS surface disappear and are not considered while other areas are indicated as having high HFO-IIS activity. This erosion process and contrast enhancement is illustrated in Fig., and not in Fig.. Fig. also demonstrate how areas of high activity, or cortex regions of interest, which are identified in the HFO surface may or may not be identified in the IIS surface, and vice versa. This further substantiates the need for a merger of these analyses for the researcher, doctor, and patient, to have a more complete understanding of the abnormal brain electrical activity. While HFO-IIS surfaces in Fig. and Fig. tend to capture high activity areas for both the HFO and IIS, this is not the case in Fig. where the highlighted region in the HFO-IIS surface does not correlate with high activity regions in either of the other surfaces. This may be indicative a previously unidentified cortex region that is contributing to the onset of seizures. The results may also show other important information regarding the placement of the electrode grid. While the HFO- IIS surface in Fig. seems to superficially represent a composite of the HFO surface and the IIS surface, a more indepth assessment reveals that it highlights an area of highactivity that is not seen on the HFO or IIS surface. This area happens to be along the rightmost column of the electrode array and may indicate that further analysis is needed with the electrode grid shifted to fully encompass this region as well as the region along the bottom row of the electrode grid, where more high activity electrodes are located The future prospects of this work will aim to incorporate fast ripples which are defined as HFO in the Hz spectral band and to incorporate this into the presented analysis. All EEG data used for this research is of CSWS because this is where clean data containing HFO is most likely to occur. Further work will include pre-ictal as well as postictal subdural EEG data. are those of the authors and do not necessarily reflect the views of the National Science Foundation or Department of Defense. REFERENCES [] S. S. Spencer, D. K. Nguyen, and R. B. Duckrow, Invasive EEG in Presurgical Evaluation of Epilepsy, Chapter of the Treatment of Epilepsy, rd ed. Hoboken, NJ: Wiley, 9, pp. 9. [] Huiskamp G. and Agirre-Arrizubieta Z., Interictal ECoG spikes as reflected in MEG, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 9. [] Torres G., McCall P., Liu C., Cabrerizo M., Adjouadi M., Parallelizing Electroencephalogram processing on a many-core platform for the detection of high frequency oscillations, th International Workshop on Unique Chips and Systems,. 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J., High-frequency oscillations in epileptic brain, Current Opinion in Neurology: April - Volume - Issue - p. [9] Adjouadi, M., et al., "Detection of interictal spikes and artifactual data through orthogonal transformations." Journal of Clinical Neurophysiology. (): -. ACKNOWLEDGMENT This work is partly supported by the National Science Foundation and the Department of Defense (DoD). Paul McCall is supported through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. The authors are also grateful for the support provided by the National Science Foundation under grants CNS-999, CNS-, and HRD-9. Any opinions, findings, and conclusions or recommendations expressed in this material