医学
工件(错误)
创伤性脑损伤
颅内压
血压
心脏病学
卷积神经网络
脑灌注压
灌注
内科学
麻醉
神经科学
人工智能
计算机科学
生物
精神科
作者
Seung-Bo Lee,Hakseung Kim,Young-Tak Kim,Frederick A. Zeiler,Peter Smielewski,Marek Czosnyka,Dong‐Joo Kim
出处
期刊:Journal of Neurosurgery
[American Association of Neurological Surgeons]
日期:2019-05-17
卷期号:132 (6): 1952-1960
被引量:22
标识
DOI:10.3171/2019.2.jns182260
摘要
Monitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.
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