ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2018
Utilization of independent component analysis for accurate pathological ripple detection in intracranial EEG recordings recorded extra- and intra-operatively.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 39 citations in OpenAlex.
- High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates.BMJ paediatrics open · 2026Review
- Consensus-based Sharp-Wave Ripple detection and its application in an alcohol administration model.Frontiers in systems neuroscience · 2026Article
- Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks.medRxiv : the preprint server for health sciences · 2024Article
- Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer.Epilepsia · 2024Article
- Simulated resections and responsive neurostimulator placement can optimize postoperative seizure outcomes when guided by fast ripple networks.Brain communications · 2024Article
- Fast ripples reflect increased excitability that primes epileptiform spikes.Brain communications · 2023Article
- Delta oscillation coupled propagating fast ripples precede epileptiform discharges in patients with focal epilepsy.Neurobiology of disease · 2022Article
- Graph theoretical measures of fast ripples support the epileptic network hypothesis.Brain communications · 2022Article
- Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions.Scientific reports · 2021Article
- Effects of depth electrode montage and single-pulse electrical stimulation sites on neuronal responses and effective connectivity.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2020Article
- Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe.Frontiers in neurology · 2020Article
- Ripples Have Distinct Spectral Properties and Phase-Amplitude Coupling With Slow Waves, but Indistinct Unit Firing, in Human Epileptogenic Hippocampus.Frontiers in neurology · 2020Article
- Does data cleaning improve brain state classification?Journal of neuroscience methods · 2019Article
- Localizing epileptogenic regions using high-frequency oscillations and machine learning.Biomarkers in medicine · 2019Review
- Localization of the Epileptogenic Zone Using High Frequency Oscillations.Frontiers in neurology · 2019Review
- Nonictal EEG biomarkers for diagnosis and treatment.Epilepsia open · 2018Article
- Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding.Epilepsy & behavior : E&B · 2018Article
- Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2018Article
- A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2018Article
- Generalizability of High Frequency Oscillation Evaluations in the Ripple Band.Frontiers in neurology · 2018Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop and validate a detector that identifies ripple (80-200 Hz) events in intracranial EEG (iEEG) recordings in a referential montage and utilizes independent component analysis (ICA) to eliminate or reduce high-frequency artifact contamination. Also, investigate the correspondence of detected ripples and the seizure onset zone (SOZ).
methodsiEEG recordings from 16 patients were first band-pass filtered (80-600 Hz) and Infomax ICA was next applied to derive the first independent component (IC1). IC1 was subsequently pruned, and an artifact index was derived to reduce the identification of high-frequency events introduced by the reference electrode signal. A Hilbert detector identified ripple events in the processed iEEG recordings using amplitude and duration criteria. The identified ripple events were further classified and characterized as true or false ripple on spikes, or ripples on oscillations by utilizing a topographical analysis to their time-frequency plot, and confirmed by visual inspection.
resultsThe signal to noise ratio was improved by pruning IC1. The precision of the detector for ripple events was 91.27 ± 4.3%, and the sensitivity of the detector was 79.4 ± 3.0% (N = 16 patients, 5842 ripple events). The sensitivity and precision of the detector was equivalent in iEEG recordings obtained during sleep or intra-operatively. Across all the patients, true ripple on spike rates and also the rates of false ripple on spikes, that were generated due to filter ringing, classified the seizure onset zone (SOZ) with an area under the receiver operating curve (AUROC) of >76%. The magnitude and spectral content of true ripple on spikes generated in the SOZ was distinct as compared with the ripples generated in the NSOZ (p < .001).
conclusionsUtilizing ICA to analyze iEEG recordings in referential montage provides many benefits to the study of high-frequency oscillations. The ripple rates and properties defined using this approach may accurately delineate the seizure onset zone. SIGNIFICANCE: Strategies to improve the spatial resolution of intracranial EEG and reduce artifact can help improve the clinical utility of HFO biomarkers.
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