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用于自动体外除颤器的新旧心室颤动检测算法的可靠性

Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators.

作者信息

Amann Anton, Tratnig Robert, Unterkofler Karl

机构信息

Department of Anesthesia and General Intensive Care, Innsbruck Medical University, Anichstr. 35, A-6020 Innsbruck, Austria.

出版信息

Biomed Eng Online. 2005 Oct 27;4:60. doi: 10.1186/1475-925X-4-60.

Abstract

BACKGROUND

A pivotal component in automated external defibrillators (AEDs) is the detection of ventricular fibrillation by means of appropriate detection algorithms. In scientific literature there exists a wide variety of methods and ideas for handling this task. These algorithms should have a high detection quality, be easily implementable, and work in real time in an AED. Testing of these algorithms should be done by using a large amount of annotated data under equal conditions.

METHODS

For our investigation we simulated a continuous analysis by selecting the data in steps of one second without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and the files 7001-8210 of the AHA database. All algorithms were tested under equal conditions.

RESULTS

For 5 well-known standard and 5 new ventricular fibrillation detection algorithms we calculated the sensitivity, specificity, and the area under their receiver operating characteristic. In addition, two QRS detection algorithms were included. These results are based on approximately 330,000 decisions (per algorithm).

CONCLUSION

Our values for sensitivity and specificity differ from earlier investigations since we used no preselection. The best algorithm is a new one, presented here for the first time.

摘要

背景

自动体外除颤器(AED)的一个关键组成部分是通过适当的检测算法来检测心室颤动。在科学文献中,存在各种各样处理此任务的方法和思路。这些算法应具有较高的检测质量,易于实现,并能在AED中实时运行。这些算法的测试应在相同条件下使用大量带注释的数据进行。

方法

为了我们的研究,我们通过以一秒为步长选择数据而不进行任何预选来模拟连续分析。我们使用了完整的BIH - MIT心律失常数据库、CU数据库以及AHA数据库的7001 - 8210文件。所有算法均在相同条件下进行测试。

结果

对于5种著名的标准心室颤动检测算法和5种新的心室颤动检测算法,我们计算了它们的灵敏度、特异性以及接收者操作特征曲线下的面积。此外,还纳入了两种QRS检测算法。这些结果基于大约330,000次决策(每种算法)。

结论

由于我们未进行预选,我们的灵敏度和特异性值与早期研究不同。最佳算法是一种新算法,在此首次提出。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e07c/1283146/5a3e7973a8fb/1475-925X-4-60-1.jpg

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