Methods for Detecting High-Frequency Oscillations in Ongoing Brain Signals: Application to the Determination of Epileptic Seizure Onset Zones

Epilepsy is a neurological disorder with varied expression. Patients with focal onset seizures that are resistant to medications can benefit from ablative surgery. However, localization of the seizure onset zone (SOZ) and characterization of propagation to secondary areas can be challenging. The present study aimed at developing the appropriate signal processing methodology to detect bursts of interictal high-frequency oscillations (HFOs), as a possible signature of the SOZ, in patients with drug-resistant partial epilepsy. Additionally, invasive interictal and ictal intracranial electroencephalography (iEEG) data and non-invasive electromagnetic source imaging with magnetoencephalography (MEG) data from three subjects were analyzed. We developed a novel algorithm that extracts HFO bursts from the envelope of iEEG and MEG traces in the [80-300] Hz range. Clusters of HFO events across multichannel iEEG traces were subsequently analyzed to investigate their relative time delays and to infer possible propagation patterns during the interictal period and episodes of ictal onset (iEEG only). The location of iEEG electrodes sustaining the HFO bursts were labeled with respect to the chronometry of the local HFOs. The recording site bearing the smallest rank was labeled as the lead generator of HFO discharges. The aim of using MEG traces was essentially to determine probable SOZ locations non-invasively by extending the results obtained with iEEG. We proposed a new metric referred to as `spiking index\u27 that was computed at each cortical site in the vicinity of iEEG electrode locations (iEEG and MEG data were obtained for the same patients: iEEG was considered as the standard of reference for MEG results). The sensitivity and specificity of the HFO detector operating from ongoing brain traces were evaluated. Our results indicate that higher values of spiking index and higher rates of HFOs corresponded to brain regions that were identified independently as the SOZ by an expert clinician and as determined by the location and extent of the cortical resection that freed the patients from the seizures. Interictal and ictal iEEG HFO localization showed good concordance with the location of resected areas. The use of interictal data only, if used for surgical planning, would reduce the time required for making decisions regarding the resection of cortex and improve the chances of success of surgery in making patients become seizure-free. Obtaining iEEG data is invasive, with possible risks to the patients, and requires an expensive procedure. Another fundamental disadvantage of iEEG is that the implanted electrode grids and strips needed to cover the supposed abnormal cortical areas for proper determination of the SOZ. Our results indicate that the spiking index and rate map obtained from MEG source maps may provide a non-invasive alternative for determination of the SOZ and may provide greater accuracy to the placement of the implantable electrodes, and eventually avoid an invasive exploratory procedure before surgery

EEG/MEG Sparse Source Imaging and Its Application in Epilepsy

This dissertation is a summary of my Ph.D. work on the development of sparse source imaging technologies based on electroencephalography (EEG) and magneto-encephalography (MEG) and their application to noninvasively reconstruct brain activation from external surface measurements. Conventional sparse source imaging (SSI) methods using the ℓ1-norm regularization to enforce sparseness in the original source domain leads to over-focused solutions and causes bias in estimating spatially extended brain sources. I address the over-focused issue in the ℓ1-norm regularization technique framework by exploring sparseness in the transform domains. First, I apply a SSI method that uses the variation transform, i.e. V-SSI, on clinical MEG interictal recordings from partial epilepsy patients. Estimated epileptic sources by V-SSI are validated using clinical pre-surgical evaluation data and surgical outcomes. Second, I implement a novel face-based wavelet transform, which can efficiently compress brain activation signals into sparse representations on a multi-resolution cortical source model, into the SSI technology framework. The proposed wavelet-based SSI (W-SSI) demonstrates a significantly improved ability in inferring both brain source locations and extents as compared with conventional ℓ2-norm regularizations in obtaining EEG/MEG inverse solutions and other SSI technologies. Furthermore, the face-based wavelet also indicates better performance than a previously reported vertex-based wavelet in W-SSI. I evaluate the W-SSI method and conduct the comparison studies using both simulations and real data collected from partial epilepsy patients. Lastly, I further propose the concept of using multiple transforms in the SSI technology framework and investigated a new SSI method by enforcing sparseness in both variation and face-based wavelet domains, termed as VW-SSI. I conduct simulation studies, which demonstrate that VW-SSI has significantly better detection accuracies in both source locations and extents than conventional ℓ2-norm regularizations and other SSI methods, including SSI, V-SSI, and W-SSI. I further validate the VW-SSI method using clinical MEG data from both language and motor experiments collected from epilepsy patients again to localize their important functional brain areas. The results indicate that VW-SSI provides a performance advantage in detecting neural phenomena that have been extremely difficult to recognize by other EEG/MEG inverse solutions. It thus suggests that the sparse source imaging technique is promising to serve as a non-invasive tool in assisting pre-surgical planning for partial epilepsy patients

Advances in epilepsy monitoring by detection and analysis of brain epileptiform discharges

Brain interictal and pre-ictal epileptiform discharges (EDs) are transient events occurred between two or before seizure onsets visible in intracranial electroencephalographs. In the diagnosis of epilepsy and localization of seizure sources, both interictal and ictal recordings are extremely informative. For this propose, computerized intelligent spike and seizure detection techniques have been researched and are constantly improving. This is not only to detect more EDs from over the scalp but also to classify epileptic and non-epileptic discharges. Tensor factorization and deep learning are two advanced and powerful techniques which have been recently suggested for ED detection. Here, our main contribution is to review recent ED detection methods with emphasis on multi-way analysis and deep learning approaches. These techniques have opened a new window to the epilepsy diagnosis and management spheres

Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier