使用运动伪影减少系统在不间断心肺复苏期间进行节律辨别。

Rhythm discrimination during uninterrupted CPR using motion artifact reduction system.

作者信息

Berger Ronald D, Palazzolo James, Halperin Henry

机构信息

Department of Medicine, The Johns Hopkins University School of Medicine, 600N. Wolfe Street/Carnegie 592, Baltimore, MD 21287-0409, USA.

出版信息

Resuscitation. 2007 Oct;75(1):145-52. doi: 10.1016/j.resuscitation.2007.03.007. Epub 2007 Apr 30.

DOI:10.1016/j.resuscitation.2007.03.007

PMID:17467872

Abstract

BACKGROUND

Due to motion artifact in the ECG caused by chest compressions automatic external defibrillators (AEDs) have difficulty recognizing ventricular fibrillation (VF) during cardiopulmonary resuscitation (CPR). Frequent interruption of CPR is required for artifact-free ECG interpretation, but these interruptions reduce the efficacy of CPR. We developed a motion artifact reduction system (MARS), based on adaptive noise cancellation techniques, for use during CPR. We hypothesized that this system would allow for automated rhythm discrimination during uninterrupted CPR.

METHODS AND RESULTS

Thirteen swine underwent CPR during normal sinus rhythm (NSR) and repeated inductions of VF and asystole, using an automated device that uses a load-distributing band to compress the anterior chest. A single ECG lead and the instantaneous compression force signal were sampled during continuous CPR and fed to MARS, which in turn provided a filtered ECG signal in which artifacts that correlated with compression force were suppressed. The filtered and unfiltered ECGs were then fed simultaneously, and in real time, to three pairs of defibrillators with rhythm discrimination functions. During CPR, non-shockable rhythms were correctly classified by the defibrillators in 59 of 63 instances using the raw ECG, and 60 of 63 instances using the MARS-filtered ECG (p=N.S.). During CPR, VF was correctly classified in 35 of 222 attempts using the raw ECG, and in 310 of 318 cases using the MARS-filtered ECG (p<0.001). As control, when CPR was not applied, all rhythms were correctly identified by each defibrillator using either the raw ECG or the filtered ECG.

CONCLUSIONS

Motion artifact reduction by adaptive noise cancellation allows for recognition of VF during uninterrupted automated CPR, while this is rarely possible based on the raw ECG. Incorporation of this signal processing strategy may obviate the need for interruptions in chest compression and thus enhance CPR efficacy.

摘要

背景

由于胸部按压导致心电图（ECG）出现运动伪影，自动体外除颤器（AED）在心肺复苏（CPR）期间难以识别心室颤动（VF）。为了获得无伪影的心电图解读，需要频繁中断心肺复苏，但这些中断会降低心肺复苏的效果。我们基于自适应噪声消除技术开发了一种运动伪影减少系统（MARS），用于心肺复苏期间。我们假设该系统将允许在不间断的心肺复苏期间进行自动节律判别。

方法与结果

13头猪在正常窦性心律（NSR）以及反复诱发心室颤动和心搏停止期间接受心肺复苏，使用一种通过负载分配带对前胸进行按压的自动装置。在持续心肺复苏期间，采集单导联心电图和瞬时按压力信号，并将其输入MARS，MARS进而提供一个经过滤波的心电图信号，其中与按压力相关的伪影被抑制。然后将经过滤波和未经过滤波的心电图同时实时输入三对具有节律判别功能的除颤器。在心肺复苏期间，使用原始心电图时，除颤器在63例中有59例正确分类了不可电击节律，使用经MARS滤波的心电图时，63例中有60例正确分类（p=无显著差异）。在心肺复苏期间，使用原始心电图时，222次尝试中有35次正确分类了心室颤动，使用经MARS滤波的心电图时，318例中有310例正确分类（p<0.001）。作为对照，当不进行心肺复苏时，每个除颤器使用原始心电图或滤波后的心电图都能正确识别所有节律。