Quantification of spontaneous and evoked HFO's in SEEG recording and prospective for pre-surgical diagnostics. Case study

Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework

PURPOSE

High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters.

METHODS

Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies.

RESULTS

From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors.

CONCLUSIONS

Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.

Noninvasive Localization of High-Frequency Oscillations in Children with Epilepsy: Validation against Intracranial Gold-Standard

INTRODUCTION

Patients with medically refractory epilepsy (MRE) need surgical resection of the epileptogenic zone (EZ) to gain seizure-freedom. High-frequency oscillations (HFOs, > 80 Hz) are promising biomarkers of the EZ that are typically localized using intracranial electroencephalography (icEEG). The goal of this study was to localize the cortical generators of HFOs non-invasively using high-density (HD) EEG and magnetoencephalography (MEG) and validate the localization against the gold-standard given by the icEEGdefined HFO-zone.

METHODS

We analyzed simultaneous HDEEG and MEG data from six children with MRE who underwent icEEG and surgery. We detected interictal HFOs (80-160 Hz) on HD-EEG and MEG separately, using an inhouse automatic detector followed by visual human review, and distinguished between HFOs with and without spikes. We localized the cortical generators of each HFO on HD-EEG or MEG using the wavelet Maximum Entropy on the Mean (wMEM). For the HFOs localized in the brain area covered by icEEG, we estimated the localization error (E_loc) with respect to the gold-standard, and classified them as either concordant (E_loc≤15mm) or not.

RESULTS

We found that: (i) HD-EEG presented a higher rate of HFOs than MEG (1 vs 0.5 HFOs/min, p=0.031); (ii) HFOs without spikes were more likely to be localized outside the brain regions of interest (i.e. covered by icEEG) than HFOs with spikes; and (iii) both HD-EEG and MEG showed high precision to the gold-standard (92% and 96%).

CONCLUSION

We reported quantitative evidence that HDEEG and MEG can localize the HFO cortical generators with high precision to the icEEG gold-standard in children with MRE, suggesting that they may possibly limit the need for icEEG prior to surgery. We also showed that HFOs with spikes on HD-EEG/MEG are more likely to be epileptogenic than those independent from spikes, which may represent physiological events from normal brain.

A neural mass model of spontaneous burst suppression and epileptic seizures

The paper presents a neural mass model that is capable of simulating the transition to and from various forms of paroxysmal activity such as burst suppression and epileptic seizure-like waveforms. These events occur without changing parameters in the model. The model is based on existing neural mass models, with the addition of feedback of fast dynamics to create slowly time varying parameters, or slow states. The goal of this research is to establish a link between system properties that modulate neural activity and the fast changing dynamics, such as membrane potentials and firing rates that can be manipulated using electrical stimulation. Establishing this link is likely to be a necessary component of a closed-loop system for feedback control of pathological neural activity.

Single Pulse Electrical Stimulation to identify epileptogenic cortex: Clinical information obtained from early evoked responses

OBJECTIVE

Single Pulse Electrical Stimulation (SPES) probes epileptogenic cortex during electrocorticography. Two SPES responses are described: pathological delayed responses (DR, >100 ms) associated with the seizure onset zone (SOZ) and physiological early responses (ER, <100 ms) that map cortical connectivity. We analyzed properties of ERs, including frequencies >80 Hz, in the SOZ and seizure propagation areas.

METHODS

We used data from 12 refractory epilepsy patients. SPES consisted of 10 pulses of 1 ms, 4-8 mA and 5s interval on adjacent electrodes pairs. Data were available at 2048 samples/s for six and 512 samples/s (22 bits) for eight patients and analyzed in the time-frequency (TF) and time-domain (TD).

RESULTS

Electrodes with ERs were stronger associated with SOZ than non-SOZ electrodes. ERs with frequency content >80 Hz exist and are specific for SOZ channels. ERs evoked by stimulation of seizure onset electrodes were associated with electrodes involved in seizure propagation.

CONCLUSION

Analysis of ERs can reveal aspects of pathology, manifested by association with seizure propagation and areas with high ER numbers that coincide with the SOZ.

SIGNIFICANCE

Not only DRs, but also ERs could have clinical value for mapping epileptogenic cortex and help to unravel aspects of the epileptic network.

Human intracranial high frequency oscillations (HFOs) detected by automatic time-frequency analysis

OBJECTIVES

High frequency oscillations (HFOs) have been proposed as a new biomarker for epileptogenic tissue. The exact characteristics of clinically relevant HFOs and their detection are still to be defined.

METHODS

We propose a new method for HFO detection, which we have applied to six patient iEEGs. In a first stage, events of interest (EoIs) in the iEEG were defined by thresholds of energy and duration. To recognize HFOs among the EoIs, in a second stage the iEEG was Stockwell-transformed into the time-frequency domain, and the instantaneous power spectrum was parameterized. The parameters were optimized for HFO detection in patient 1 and tested in patients 2-5. Channels were ranked by HFO rate and those with rate above half maximum constituted the HFO area. The seizure onset zone (SOZ) served as gold standard.

RESULTS

The detector distinguished HFOs from artifacts and other EEG activity such as interictal epileptiform spikes. Computation took few minutes. We found HFOs with relevant power at frequencies also below the 80-500 Hz band, which is conventionally associated with HFOs. The HFO area overlapped with the SOZ with good specificity > 90% for five patients and one patient was re-operated. The performance of the detector was compared to two well-known detectors.

CONCLUSIONS

Compared to methods detecting energy changes in filtered signals, our second stage - analysis in the time-frequency domain - discards spurious detections caused by artifacts or sharp epileptic activity and improves the detection of HFOs. The fast computation and reasonable accuracy hold promise for the diagnostic value of the detector.