Kurtosis-Based Detection of Intracranial High-Frequency Oscillations for the Identification of the Seizure Onset Zone

Abstract

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.

Double-Step Machine Learning Based Procedure for HFOs Detection and Classification

“…So far, HFOs have been differentiated in two different groups: ripples, oscillations in the range between 80 and 250 Hz, and fast ripples, oscillations in the range between 250 Hz and 500 Hz. The main asset of HFOs is not only their clinical application for presurgical identification of the seizure onset zone (SOZ, which can be considered as a close topological estimate of the epileptogenic zone [2]) but also as a predictor of clinical outcomes of cortex resection [3].…”

“…This family of classifiers makes it possible to generalize well to new data [47] and they are more robust to overfitting than individual trees because each node does not see all the features at the same time [46]. In this case, the number of trees (100, 200), the maximum number of levels in tree (5,10,20), the minimum number of samples required to split a node (2,5,10), and the minimum number of samples required at each leaf node (1, 2, 4) have been chosen for optimization.…”

EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy

“…After signal preprocessing and segmentation, discrete wavelet transform is computed on 1-s signal windows. The preliminary data showed high-specificity and sensitivity (96.03 and 81.94%, respectively) using complex Morlet transform ( Quitadamo et al, 2018 ). For each channel and each window, the scalogram is first computed, representing the percentage energy of each wavelet coefficient and then, for each frequency bin, the algorithm computes the spectral kurtosis, which reflects the presence of transient activities in a signal and which can be used to identify signal properties in the frequency domain ( Antoni, 2006 ).…”

“…The theoretical approach behind the algorithm was described in a recent paper by our group ( Quitadamo et al, 2018 ). Nonetheless, in this section we briefly summarize the innovative nature of the algorithm used for the detection of HFOs that constitutes the backbone of the whole EPINETLAB software.…”

“…In the HFO Detection GUI ( Figure 6 ), the user can choose between the kurtosis-based method developed by our group ( Quitadamo et al, 2018 ) and presented here and classical Staba detector ( Staba et al, 2002 ). This has been used in the past as benchmark since it was the first algorithm to provide a semi-automatic detection of HFOs.…”