Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals
Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals
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DOI：
10.1088/0967-3334/37/8/1233
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发表时间：
2016-08-01
影响因子：
3.2
通讯作者：
Walter, Marian
中科院分区：
文献类型：
--
作者：
Antink, Christoph Hoog;Leonhardt, Steffen;Walter, Marian
错误的心律失常警报对当今ICU的护理质量构成了重大威胁。因此，PhysioNet/Computing in Cardiology Challenge 2015旨在通过利用患者监护仪记录的多模态心脏信号来减少误报。心搏停止，极端心动过缓，极端心动过速，心室扑动/颤动以及室性心动过速的假警报将减少使用两个心电图通道，多达两个心脏信号的机械起源以及呼吸signal.In本文中，一种方法相结合的多模态节律估计和机器学习。利用标准短时自相关和鲁棒的逐拍间隔估计，分析了信号的自相似性。特别地，导出心跳间隔以及质量测量，其使用基本数学运算（最小值、平均值、最大值等）进一步量化。此外，从图像处理领域的方法，二维傅立叶变换结合主成分分析，用于降维。对线性判别分析和随机森林等几种机器学习方法进行了评估，并采用一种与报警无关的减少策略，在一个隐藏数据集上实现了65.52分的整体虚警减少。采用警报特定策略，实现了78.20的总体实时评分，真阳性率为95%，真阴性率为78%。虽然某些类别的结果仍需改进，但极端心动过速的假警报被抑制，灵敏度和特异性均为100%。
False arrhythmia alarms pose a major threat to the quality of care in today's ICU. Thus, the PhysioNet/Computing in Cardiology Challenge 2015 aimed at reducing false alarms by exploiting multimodal cardiac signals recorded by a patient monitor. False alarms for asystole, extreme bradycardia, extreme tachycardia, ventricular flutter/fibrillation as well as ventricular tachycardia were to be reduced using two electrocardiogram channels, up to two cardiac signals of mechanical origin as well as a respiratory signal.In this paper, an approach combining multimodal rhythmicity estimation and machine learning is presented. Using standard short-time autocorrelation and robust beat-to-beat interval estimation, the signal's self-similarity is analyzed. In particular, beat intervals as well as quality measures are derived which are further quantified using basic mathematical operations (min, mean, max, etc). Moreover, methods from the realm of image processing, 2D Fourier transformation combined with principal component analysis, are employed for dimensionality reduction. Several machine learning approaches are evaluated including linear discriminant analysis and random forest.Using an alarm-independent reduction strategy, an overall false alarm reduction with a score of 65.52 in terms of the real-time scoring system of the challenge is achieved on a hidden dataset. Employing an alarm-specific strategy, an overall real-time score of 78.20 at a true positive rate of 95% and a true negative rate of 78% is achieved. While the results for some categories still need improvement, false alarms for extreme tachycardia are suppressed with 100% sensitivity and specificity.