标题:Signal quality and data fusion for false alarm reduction in the intensive care unit
作者:Li, Qiao; Clifford, Gari D.
作者机构:[Li, Qiao; Clifford, Gari D.] Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Oxford OX3 7DQ, England.; [Li, Qiao] Shandong Univ, Sch Med, Inst Biomed 更多
通讯作者:Clifford, GD
通讯作者地址:[Clifford, GD]Univ Oxford, Inst Biomed Engn, Dept Engn Sci, Old Rd Campus Res Bldg,Off Roosevelt Dr, Oxford OX3 7DQ, England.
来源:JOURNAL OF ELECTROCARDIOLOGY
出版年:2012
卷:45
期:6
页码:596-603
DOI:10.1016/j.jelectrocard.2012.07.015
关键词:False alarm reduction; Signal quality assessment; Genetic algorithm;; Relevance vector machine; Intensive care unit
摘要:Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required. (C) 2012 Elsevier Inc. All rights reserved.
收录类别:SCOPUS;SCIE
WOS核心被引频次:36
Scopus被引频次:48
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867842919&doi=10.1016%2fj.jelectrocard.2012.07.015&partnerID=40&md5=ba13b06df6b27ae533fb36f809af7e3c
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