Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

Abstract

Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

“…If skewness is greater than zero, the largest number of data is on the left side of the curve representing the probability distribution. A skewness that is different from zero may indicate an existing eye blink artifact (Xiang et al, 2020).…”

Diagnostic added value of interictal magnetic source imaging in presurgical evaluation of persons with epilepsy: A prospective blinded study

“…While ictal and interictal localizations show a high degree of concordance, ictal MEG has increased specificity for the invasively determined seizure onset zone [28]. Even though MEG is largely used for source localization of ictal or interictal epileptiform discharges, alternate methods of analysis such as high-frequency oscillations are increasingly gaining traction [30][31][32][33][34][35][36]. These tools, along with further advances in MEG hardware, offer further scope for enhancing and evaluating the clinical utility of MEG [37][38][39].…”

Distinguishing false and true positive detections of high frequency oscillations

“…Analysis of the background is also involved in studies testing the utility of restricting to only HFO occurring on a spike ( Wang et al 2013 ) or repetitive pattern ( Liu et al 2018 ). An alternate approach is to use analysis of background as an alternative to using an HFO detector for assessing HFO information (thus circumventing the challenge of false positive detections), and instead compute features of the combined background plus HFO signal ( Akiyama et al 2012 , Geertsema et al 2015 , Mooij et al 2020 , Xiang et al 2020 ). Unique to the analysis of this manuscript is that we directly assessed the background properties to learn characteristics of false positive HFOs due to neural sources, rather than using it solely for establishing a baseline, for removing false positives due to artifacts (non-neural sources) or as an alternative to HFO detection.…”