Reducing False Arrhythmia Alarms Using Robust Interval Estimation and Machine Learning

被引：0

作者：

Antink, Christoph Hoog ^{[1
]}

Leonhardt, Steffen ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Philips Chair Med Informat Technol, Aachen, Germany

来源：

2015 COMPUTING IN CARDIOLOGY CONFERENCE (CINC) | 2015年 / 42卷

关键词：

HEART BEATS;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Reducing false arrhythmia alarms in the intensive care unit is the objective of the PhysioNet/Computing in Cardiology Challenge 2015. In this paper, an approach is presented that analyzes multimodal cardiac signals in terms of their beat-to-beat intervals as well as their average rhythmicity. Based on this analysis, several features in time and frequency domain are extracted and used for subsequent machine learning. Results show that alarm-specific strategies proved optimal for different types of arrhythmia and that obtained scores varied: While the score for reducing false ventricular tachycardia alarms was 68.91, false extreme tachycardia alarms could be suppressed with perfect accuracy. Overall, a top score of 75.55 / 75.18 could be achieved for real-time / retrospective false alarm reduction.

引用

页码：285 / 288

页数：4

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