AI Article Synopsis

  • High-frequency oscillations (HFOs), categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), serve as potential biomarkers for identifying seizure-prone brain areas in patients with epilepsy.
  • Researchers analyzed HFO data from five epilepsy patients using an automated algorithm, focusing on changes in HFO characteristics across different seizure-related time periods (interictal, preictal, ictal, and postictal).
  • The study found significant, patient-specific fluctuations in HFO morphology and rates, indicating the potential for these temporal changes to inform customized seizure prediction methods and enhance understanding of seizure mechanisms.

Article Abstract

High-frequency (100-500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3763087PMC
http://dx.doi.org/10.1152/jn.01009.2012DOI Listing

Publication Analysis

Top Keywords

temporal changes
12
fast ripples
12
intracranial electrodes
8
mixed frequency
8
frequency events
8
ripples fast
8
ripples mixed
8
changes hfo
8
hfo features
8
hfos
7

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!