ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2018
A method for the topographical identification and quantification of high frequency oscillations in intracranial electroencephalography recordings.
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 26 papers.
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Who cites it
26 citing papers in PubMed, 46 citations in OpenAlex.
- Epileptogenicity alters intrahippocampal ripple propagation.medRxiv : the preprint server for health sciences · 2026Article
- Preventive effects of transcranial photobiomodulation on epileptogenesis in a kainic acid-induced rat epilepsy model.Experimental neurology · 2025Article
- High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance.Frontiers in neurology · 2025Article
- Electrode Surface Area Impacts Measurement of High Frequency Oscillations in Human Intracranial EEG.IEEE transactions on bio-medical engineering · 2024Article
- 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
- Connectivity of high-frequency bursts as SOZ localization biomarker.Frontiers in network physiology · 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
- An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.Epilepsia open · 2022Article
- Graph theoretical measures of fast ripples support the epileptic network hypothesis.Brain communications · 2022Article
- Spatial and temporal profile of high-frequency oscillations in posttraumatic epileptogenesis.Neurobiology of disease · 2021Article
- Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions.Scientific reports · 2021Article
- Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.Epilepsia · 2020Review
- Ripples Have Distinct Spectral Properties and Phase-Amplitude Coupling With Slow Waves, but Indistinct Unit Firing, in Human Epileptogenic Hippocampus.Frontiers in neurology · 2020Article
- Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision.Frontiers in neuroscience · 2020Article
- Does data cleaning improve brain state classification?Journal of neuroscience methods · 2019Article
- High Frequency Oscillations in Epilepsy: Detection Methods and Considerations in Clinical Application.Journal of epilepsy research · 2019Review
- Localizing epileptogenic regions using high-frequency oscillations and machine learning.Biomarkers in medicine · 2019Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop a reliable software method using a topographic analysis of time-frequency plots to distinguish ripple (80-200 Hz) oscillations that are often associated with EEG sharp waves or spikes (RonS) from sinusoid-like waveforms that appear as ripples but correspond with digital filtering of sharp transients contained in the wide bandwidth EEG.
methodsA custom algorithm distinguished true from false ripples in one second intracranial EEG (iEEG) recordings using wavelet convolution, identifying contours of isopower, and categorizing these contours into sets of open or closed loop groups. The spectral and temporal features of candidate groups were used to classify the ripple, and determine its duration, frequency, and power. Verification of detector accuracy was performed on the basis of simulations, and visual inspection of the original and band-pass filtered signals.
resultsThe detector could distinguish simulated true from false ripple on spikes (RonS). Among 2934 visually verified trials of iEEG recordings and spectrograms exhibiting RonS the accuracy of the detector was 88.5% with a sensitivity of 81.8% and a specificity of 95.2%. The precision was 94.5% and the negative predictive value was 84.0% (N = 12). Among, 1,370 trials of iEEG recording exhibiting RonS that were reviewed blindly without spectrograms the accuracy of the detector was 68.0%, with kappa equal to 0.01 ± 0.03. The detector successfully distinguished ripple from high spectral frequency 'fast ripple' oscillations (200-600 Hz), and characterize ripple duration and spectral frequency and power. The detector was confounded by brief bursts of gamma (30-80 Hz) activity in 7.31 ± 6.09% of trials, and in 30.2 ± 14.4% of the true RonS detections ripple duration was underestimated.
conclusionsCharacterizing the topographic features of a time-frequency plot generated by wavelet convolution is useful for distinguishing true oscillations from false oscillations generated by filter ringing. SIGNIFICANCE: Categorizing ripple oscillations and characterizing their properties can improve the clinical utility of the biomarker.
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