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
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.