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

Lucia Rita Quitadamo,Roberto Mai,Stefano Seri,Francesco Cardinale,Veronica Pelliccia,Francesca Gozzo

doi:10.1142/s0129065718500016

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%.

Highlights

Epilepsy is a complex and heterogeneous neurological disorder which affects approximately 50 million people worldwide and 2.4 million people are diagnosed with epilepsy every year (World HealthOrganization, http://www.who.int/mediacentre/fact sheets/fs999/en/)
To address the issues raised above and to facilitate data reduction and identification of candidate channels for High-Frequency Oscillations (HFOs) analysis, we describe a method for the detection of HFOs in intracranial EEG (iEEG) that uses spectral kurtosis as criterion for restricting the search of HFOs to a subset of relevant candidate channels with specific time–frequency properties
We describe a novel method for the detection of HFOs in the ripple frequency band (80– 250 Hz) of the iEEG and the identification of the Seizure Onset Zone (SOZ) developed as a complete plugin for the EEGLAB suite

Summary

Introduction

Epilepsy is a complex and heterogeneous neurological disorder which affects approximately 50 million people worldwide and 2.4 million people are diagnosed with epilepsy every year (World HealthOrganization, http://www.who.int/mediacentre/fact sheets/fs999/en/). “the minimum amount of cortex that must be. In selected drug-resistant patients, a surgical option can be offered after accurate identification of the Epileptogenic Zone (EZ), i.e. This is an Open Access article published by World Scientific Publishing Company. In the absence of a single diagnostic technique capable of identifying the entire EZ directly, in clinical practice, the Seizure Onset Zone (SOZ), i.e. the area of the cortex from which seizures originate, is used as a surrogate of the EZ. A definitive marker (functional or structural) that can exactly delineate the SOZ and optimize pre-surgical evaluation and reduce pre- and post-operative morbidity is still lacking.[2]

Methods

Discussion

Conclusion