Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.

Autor: Fazli Besheli B; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA., Sha Z; Department of Neurology, University of Minnesota, Minneapolis, MN, 55401, USA., Gavvala JR; Department of Neurology, UT Health, Houston, TX, 77030, USA., Karamursel S; Department of Physiology, School of Medicine, Koç Üniversitesi, Istanbul, Türkiye., Quach M; Department of Neurology, Texas Children's Hospital, Houston, TX, 77030, USA., Swamy CP; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA., Ayyoubi AH; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.; Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, 55455, USA., Goldman AM; Department of Neurology-Neurophysiology, Baylor College of Medicine, Houston, TX, 77030, USA., Curry DJ; Department of Neurosurgery, Texas Children's Hospital, Houston, TX, 77030, USA., Sheth SA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA., Darrow D; Department of Neurosurgery, University of Minnesota, Minneapolis, MN, 55401, USA., Miller KJ; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA., Francis DJ; Department of Psychology, University of Houston, Houston, TX, 77030, USA., Worrell GA; Department of Neurology, Mayo Clinic, Rochester, MN, 55905, USA., Henry TR; Department of Neurology, University of Minnesota, Minneapolis, MN, 55401, USA., Ince NF; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA. Ince.Nuri@mayo.edu.; Department of Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA. Ince.Nuri@mayo.edu.
Jazyk: angličtina
Zdroj: Communications medicine [Commun Med (Lond)] 2024 Nov 25; Vol. 4 (1), pp. 243. Date of Electronic Publication: 2024 Nov 25.
DOI: 10.1038/s43856-024-00654-0
Abstrakt: Background: While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.
Methods: In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery.
Results: Our framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ.
Conclusions: These findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy.
Competing Interests: Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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