Kurtosis-based detection of intracranial high-frequency oscillations for the identification of the seizure onset zone

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

Research output: Contribution to journalArticle

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[Formula: see text]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%.

LanguageEnglish
Article number1850001
Number of pages18
JournalInternational Journal of Neural Systems
Volume28
Issue number7
Early online date26 Mar 2018
DOIs
Publication statusE-pub ahead of print - 26 Mar 2018

Fingerprint

Electroencephalography
Biomarkers
Frequency bands
Time series
Brain
Pipelines
Inspection

Bibliographical note

© The Author(s)

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

Funding: MSCA-IF-2014-EF -Marie Skłodowska-Curie Individual Fellowship.

Keywords

  • Epilepsy
  • intracranial-EEG (iEEG)
  • High-frequency Oscillations (HFOs)
  • Kurtosis
  • Wavelet transform.

Cite this

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title = "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[Formula: see text]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{\%}.",
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Kurtosis-based detection of intracranial high-frequency oscillations for the identification of the seizure onset zone. / Quitadamo, Lucia Rita; Mai, Roberto ; Gozzo, Francesca; Pelliccia, Veronica ; Cardinale, Francesco; Seri, Stefano.

In: International Journal of Neural Systems, Vol. 28, No. 7, 1850001, 26.03.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Kurtosis-based detection of intracranial high-frequency oscillations for the identification of the seizure onset zone

AU - Quitadamo, Lucia Rita

AU - Mai, Roberto

AU - Gozzo, Francesca

AU - Pelliccia, Veronica

AU - Cardinale, Francesco

AU - Seri, Stefano

N1 - © The Author(s) This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. Funding: MSCA-IF-2014-EF -Marie Skłodowska-Curie Individual Fellowship.

PY - 2018/3/26

Y1 - 2018/3/26

N2 - 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[Formula: see text]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%.

AB - 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[Formula: see text]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%.

KW - Epilepsy

KW - intracranial-EEG (iEEG)

KW - High-frequency Oscillations (HFOs)

KW - Kurtosis

KW - Wavelet transform.

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DO - 10.1142/S0129065718500016

M3 - Article

VL - 28

JO - International Journal of Neural Systems

T2 - International Journal of Neural Systems

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