Radek Janca

Detection of Interictal Epileptiform Discharges Using Signal Envelope Distribution Modelling: Application to Epileptic and Non-Epileptic Intracranial Recordings

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector’s specificity and having important implications for future development of spike detectors in general.

53. Methods of high frequency oscillations detection: Advantages and disadvantages

Background High frequency oscillations (HFOs) represent new electrographic marker of epileptogenic tissue and they are considered as a surrogate marker of seizure onset and epileptogenic zones. HFOs are recorded mainly in intracranial recordings. Visual analysis of HFOs in long-term recordings is extremely difficult due to the low signal-to-noise ratio of HFOs. Successful integration of HFOs into presurgical evaluation requires development of reliable methods of automatic HFO detection and quantification. We aimed to examine performance of three new HFO detecting algorithms and compared their performance with published detectors. Methods We implemented three published detectors which utilize RMS, line length or Hilbert transform approach to detect HFOs. We have developed additional three types of detectors which utilize short time energy estimation, Hilbert envelope and Bayesian evidence. All HFO detecting algorithms were applied to gold standard datasets and their performance quantified. Results Line length and Hilbert detectors detected the highest number of HFOs. The lowest number of the detections was achieved by RMS and energy estimating detectors. According to the results, the detectors can be divided into two groups. One group is characterized by high sensitivity. These algorithms detect nearly all the labeled HFOs events, but suffer from the high false positive detection rate. Second group of detectors have high positive prediction value but lower sensitivity. Our Hilbert envelope detector demonstrated the best performance of all evaluated detectors. Conclusions To improve the performance of detectors with high sensitivity will require to develop additional post-processing steps to remove the majority of false detections. Meanwhile detectors with low sensitivity will detect only high-amplitude HFOs. Future selection of the most appropriate algorithm for HFO detection in intracranial recordings will require detail understanding of the clinical significance of low-amplitude HFOs and major sources of false positive detections. Supported by Grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).

Automatic detection and spatial clustering of interictal discharges in invasive recordings

Interictal epileptiform discharges (spikes) represent electrographic marker of epileptogenic brain tissue. Besides ictal onsets, localization of interictal epileptiform discharges provides additional information to plan resective epilepsy surgery. The main goals of this study were: 1) to develop a reliable automatic algorithm to detect high and low amplitude interictal epileptiform discharges in intracranial EEG recordings and 2) to design a clustering method to extract spatial patterns of their propagation. For detection, we used a signal envelope modeling technique which adaptively identifies statistical parameters of signals containing spikes. Application of this technique to human intracranial EEG data demonstrated that it was superior to expert labeling and it was able to detect even small amplitude interictal epileptiform discharges. In the second task, detected spikes were clustered by principal component analysis according to their spatial distribution. Preliminary results showed that this unsupervised approach is able to identify distinct sources of interictal epileptiform discharges and has the potential to increase the yield of presurgical examination by improved delineation of the irritative zone.

Automatic detection of high-frequency oscillations in invasive recordings

High-frequency oscillations (HFOs) represent relatively new electrographic marker of epileptogenic tissue. It is starting to be used in presurgical examination to better plan surgical resection and to improve outcome of epilepsy surgery. Development of new techniques of unsupervised HFOs detection is required to further investigate the role of HFO in the pathophysiology of epilepsy and to increase the yield of presurgical examination. In this study we applied an envelope distribution modelling technique on experimental and human invasive data to detect HFOs. Application to experimental microelectrode recordings demonstrated satisfactory results with sensitivity 89.9% and false positive rate 2.1 per minute. Application of this algorithm to human invasive recordings achieved sensitivity 80%. High numbers of false positive detections required utilization of postprocessing steps to eliminate the majority of them. This study shows that envelope distribution modelling represents a promising approach to detect HFOs in intracranial recordings. Advantages of this approach are quick adjustments to changes in background activity and resistance to signal nonstationarities. However, successful application to clinical practice requires development of secondary processing steps that will decrease the rate of false positive detections.

The Sub-Regional Functional Organization of Neocortical Irritative Epileptic Networks in Pediatric Epilepsy

Between seizures, irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy.

Intraoperative thermography in safety control of the electrical stimulation mapping

The cortical Electric Stimulation Mapping (ESM) procedure is used as a standard approach to localize and continuously monitor function of the eloquent cortex and corticospinal tract during neurosurgical intervention. However, eliciting motor responses using standard ESM paradigm is frequently difficult to young children. We have thus developed and tested a novel EMS protocol, which uses intense, high frequency and short stimulation pulses. However, the intense stimulation peak-peak current (up to 100 mA) possess the potential risk of tissue damage.

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy

High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs.

35. Practical value of quantitative EEG in epilepsy surgery planning

Objective To assess whether available algorithms of quantitative EEG (qEEG) could practically help in localizing epileptogenic zone (EZ) and modify surgical planning in patients with focal intractable epilepsy. Methods We will present a case report of a 7-year-old boy with catastrophic epilepsy caused by focal cortical dysplasia located in the operculo-insular region of the right hemisphere. Due to the challenging localization of the presumed EZ, uncertain surgical borders and expected significant risks of the resection, the patient was stereotactically implanted with oblique depth electrodes. Intracranial EEG (iEEG) signal was analyzed using different qEEG methods. Our originally developed interictal epileptiform discharges (IED) detecting algorithm, which also extracts repetitive propagation patterns, was applied to localize sources of IED. We also used own network connectivity algorithm to analyze ictal (seizure) iEEG activity in detail. Employing results of qEEG analyses, 2D and 3D dynamic reconstructions of both interictal and ictal iEEG epileptiform changes were created and used to guide surgical approach. Results Both qEEG algorithms clearly proved the EZ localization in the dorso-caudal insular cortex of the right hemisphere and demonstrated sparing of initially suspected frontal opercular area. The dorso-caudal insular cortex generated 89% of IED; remaining 11% IED originated from the primary motor cortex. Surgical approach was adjusted to this qEEG-based hypothesis. Oblique depth electrodes were preserved intraoperatively to help precise targeting of the lesion. Continuous intraoperative motor-evoked potential monitoring was used to preserve motor functions. The boy has been followed for more than one year postoperatively. He is seizure-free with no motor deficit; with normal cognitive functions. Conclusions The case report demonstrates that currently available qEEG methods could help in guiding resective epilepsy surgery in complicated patients indicated for iEEG studies. We suggest our approach could increase patients’ chance to obtain seizure-free outcomes without new deficits and thus ultimately improve their quality of life. Supported by MH CZ–DRO, University Hospital Motol, Prague, Czech Republic 00064203 and IGA NT/11460-4.

25. Quantitative EEG assessment in epileptology – A possible way to improve the diagnostics and treatment

Novel and quantitative methods EEG signal analysis are being developed by close multidisciplinary collaborations between epilepsy specialists, biomedical engineers and mathematicians. Quantitative analysis of the long-term monitoring from intracranial electrodes is expected to provide precise and objective results. High performance computational algorithms will be presented, not only from technical point of view, but also to demonstrate that output of these techniques can provide quantitative and clinically relevant diagnostic information. Three types of automatic and semi-automatic algorithms of quantitative EEG analysis will be presented and their benefits for epilepsy surgery planning discussed. Interictal epileptiform discharges and high-frequency oscillations represent electrographic markers of epileptic tissue. Methods of their automatic detection can substantially facilitate analysis of multi-channel long-term intracranial recordings and extract unbiased meaningful information about spatiotemporal and morphological properties of these markers. Visual identification of seizure onset zone in intracranial recordings is challenging and prone to bias. Methods of seizure onset identification represent one of the main research directions of intracranial signal processing. It has been demonstrated that introduction of causality measures and network analysis can provide useful information about epileptic network organization. These techniques are capable to identify the seizure onset zone in both ictal and interictal recordings. Application of average Directed Transfer Function and Granger’s causality to intracranial recordings demonstrate that seizure onset zone is characterized by the disconnection from the rest of the epileptic network. Increased information yield and quantitative results lead to increased integration of the above mentioned methods into presurgical diagnosis. These methods of intracranial signal analysis can improve guiding of resective surgery in difficult-to-treat cases and offer surgery to patients formerly classified as not suitable for surgery. Supported by grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).