Wavelet-denoising of electroencephalogram and the absolute slope method: A new tool to improve electroencephalographic localization and lateralization

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Ictal high-frequency oscillations and hyperexcitability in refractory epilepsy

OBJECTIVE

High-frequency oscillations (HFOs, 80-500Hz) from intracranial electroencephalography (EEG) may represent a biomarker of epileptogenicity for epilepsy. We explored the relationship between ictal HFOs and hyperexcitability with a view to improving surgical outcome.

METHODS

We evaluated 262 patients with refractory epilepsy. Fifteen patients underwent electrode implantation, and surgical resection was performed in 12 patients using a semi-prospective design. Ictal intracranial EEGs were examined by continuous wavelet transform (CWT). Significant ictal HFOs were denoted by normalized wavelet power above the 50th percentile across all channels. Each patient underwent functional mapping with cortical electrical stimulation. Hyperexcitability was defined as the appearance of afterdischarges or clinical seizures after electrical stimulation (50Hz, biphasic, pulse width=0.5ms, 5s, 5mA).

RESULTS

Among the group of patients achieving Engel Class I/II outcome at 1+ year, the mean proportion of significant ictal HFOs among resected channels for any given patient was 69% (33.3-100%). The respective figures for conventional frequency ictal patterns (CFIPs), hyperexcitability, and radiological lesion were 68.3% (26.3-100%), 39.6% (0-100%), and 52.8% (0-100%). Statistical significance was only achieved with ictal HFOs when comparing patients with Engel Class I/II outcomes versus III/IV outcomes (12.6% vs. 4.2%, the number of channels as the denominator, p=0.005). Further analysis from all patients irrespective of the surgical outcome showed that ictal HFOs co-occurred with CFIP (p<0.001), hyperexcitability (p<0.001), and radiological lesion (p<0.001). The combination of ictal HFOs/hyperexcitability improved the sensitivity from 66.7% to 100%, and the specificity from 66.7% to 75% when compared with ictal HFOs or hyperexcitability alone.

CONCLUSIONS

We confirmed the utility of ictal HFOs in determining surgical outcome. Ictal HFOs are affiliated to cortical hyperexcitability, which may represent a pathological manifestation of epileptogenicity.

SIGNIFICANCE

Presurgical evaluation of refractory epilepsy may incorporate both ictal HFOs and cortical stimulation in determining epileptogenic foci.

P50 sensory gating and smoking in the general population

P50 gating is a major functional biomarker in research on schizophrenia and other psychiatric conditions with high smoking prevalence. It is used as endophenotype for studying nicotinic systems genetics and as surrogate endpoint measure for drug development of nicotinic agonists. Surprisingly, little is known about P50 gating in the general population and the relationship to smoking-related characteristics. In this multicenter study at six academic institutions throughout Germany, n=907 never-smokers (NS<20 cigarettes/lifetime), n=463 light smokers (LS) with Fagerström Test for Nicotine Dependence (FTND)≥4 and n=353 heavy smokers (HS, FTND<4) were randomly selected from the general population. As part of a standardized protocol for investigating the genetics of nicotine dependence (ND), an auditory P50 paradigm was applied. The main outcome measure was P50-amplitude difference followed by time-frequency analyses and functional imaging (sLORETA). Reduced P50 gating was found in HS compared to NS with LS taking an intermediate position-correlating with the degree of ND. sLORETA and time-frequency analyses indicate that high-frequency oscillations in frontal brain regions are particularly affected. With growing age, P50 gating increased in (heavy) smokers. This is the first large-scale study (normative sample data) on P50 sensory gating and smoking in the general population. Diminished gating of P50 and associated high-frequency oscillations in the frontal brain region are indications of a deficient inhibitory cortical function in nicotine-dependent smokers. The suitability and application of sensory P50 gating as functional biomarker with regard to genetic and pharmacological studies is discussed.

The removal of ocular artifacts from EEG signals using adaptive filters based on ocular source components

Ocular artifacts are the most important form of interference in electroencephalogram (EEG) signals. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the patient. In contrast, blind source separation (BSS) is a method of decomposing multiple EEG channels into an equal number of source components (SCs) by independent component analysis. The ocular artifacts significantly contribute to some SCs but not others, so uncontaminated EEG signals can be obtained by discarding some or all of the affected SCs and re-mixing the remaining components. BSS can be performed without EOG data. This study presents a novel ocular-artifact removal method based on adaptive filtering using reference signals from the ocular SCs, which avoids the need for parallel EOG recordings. Based on the simulated EEG data derived from eight subjects, the new method achieved lower spectral errors and higher correlations between original uncorrupted samples and corrected samples than the adaptive filter using EOG signals and the standard BSS method, which demonstrated a better ocular-artifact reduction by the proposed method.