Gaining Clarity on the Claritɣ Algorithm
Author:
Funder
Ceribell Inc.
Publisher
Springer Science and Business Media LLC
Subject
Critical Care and Intensive Care Medicine,Neurology (clinical)
Link
https://link.springer.com/content/pdf/10.1007/s12028-023-01797-z.pdf
Reference5 articles.
1. Villamar MF, Ayub N, Koenig SJ. Automated seizure detection in patients with cardiac arrest: a retrospective review of Ceribell rapid-EEG recordings. Neurocrit Care. 2023. https://doi.org/10.1007/s12028-023-01681-w.
2. Kamousi B, Karunakaran S, Gururangan K, Markert M, Decker B, Khankhanian P, et al. Monitoring the burden of seizures and highly epileptiform patterns in critical care with a novel machine learning method. Neurocrit Care. 2021;34(3):908–17.
3. Meldrum BS, Vigouroux RA, Brierley JB. Systemic factors and epileptic brain damage. Prolonged seizures in paralyzed, artificially ventilated baboons. Arch Neurol. 1973;29(2):82–7.
4. Vespa PM, Olson DM, John S, Hobbs KS, Gururangan K, Nie K, et al. Evaluating the clinical impact of rapid response electroencephalography: the DECIDE multicenter prospective observational clinical study. Crit Care Med. 2020;48(9):1249–57.
5. Gaspard N, Hirsch LJ. Pitfalls in ictal EEG interpretation: critical care and intracranial recordings. Neurology. 2013;80(Suppl 1):S26-42.
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1. Response to “Gaining Clarity on the Claritɣ Algorithm”;Neurocritical Care;2023-07-31
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