Alonso Erik, Eftestøl Trygve, Aramendi Elisabete, Kramer-Johansen Jo, Skogvoll Eirik, Nordseth Trond
Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway; Communications Engineering Department, University of the Basque Country UPV/EHU, Alameda Urquijo S/N, 48013 Bilbao, Spain.
Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway.
Resuscitation. 2014 Nov;85(11):1541-8. doi: 10.1016/j.resuscitation.2014.08.022. Epub 2014 Sep 4.
To propose a method which analyses the electrocardiogram (ECG) waveform of any cardiac rhythm occurring during resuscitation and computes the probability of that rhythm converting into another with better prognosis (Pdes).
Rhythm transitions occurring spontaneously or due to defibrillation were analyzed. For each possible rhythm, ventricular fibrillation/ventricular tachycardia (VF/VT), pulseless electrical activity (PEA), pulse-generating rhythm (PR) and asystole (AS), the desired and undesired transitions were defined. ECG segments corresponding to the last 3s of rhythms prior to transition were used to extract waveform features. For each rhythm type, waveform features were combined into a logistic regression model to develop a rhythm specific classifier of desired transitions. This model was the monitoring function for the Pdes. The capacity of each rhythm specific classifier to discriminate between desired and undesired transitions was evaluated in terms of area under the curve (AUC). Pdes was integrated into a state sequence representation, which structures the information of cardiac arrest episodes, to analyze the effect of therapy on patient. As a case study, the effect of optimal/suboptimal cardiopulmonary resuscitation (CPR) on Pdes was analyzed. The mean Pdes was computed for the pre- and post-CPR intervals which presented the same underlying rhythm. The relationship between the optimal/suboptimal CPR and increase/decrease of Pdes was analyzed.
The AUC was 0.80, 0.79, 0.73 and 0.61 for VF/VT, PEA, PR and AS respectively. The Pdes quantified the probability of every rhythm of the episode developing to a better state, and the evolution of Pdes was coherent with the provided therapy. The case study indicated, for most rhythms, that positive trends in the dynamic behaviour could be associated with optimal CPR, whereas the opposite seemed true for negative trends.
A method for continuous ECG waveform analysis covering all cardiac rhythms during resuscitation has been proposed. This methodology can be further developed to be used in retrospective studies of CPR techniques, and, in the future, for potentially monitoring in real time the probability of survival of patients being resuscitated.
提出一种方法，用于分析复苏期间出现的任何心律的心电图（ECG）波形，并计算该心律转变为预后更好的另一种心律的概率（Pdes）。
分析自发发生或因除颤而发生的心律转变。对于每种可能的心律，即心室颤动/室性心动过速（VF/VT）、无脉电活动（PEA）、有脉搏的心律（PR）和心搏停止（AS），定义了期望和不期望的转变。使用与转变前心律的最后3秒相对应的ECG片段来提取波形特征。对于每种心律类型，将波形特征组合到逻辑回归模型中，以开发出针对期望转变的心律特异性分类器。该模型是Pdes的监测函数。根据曲线下面积（AUC）评估每种心律特异性分类器区分期望和不期望转变的能力。将Pdes整合到一个状态序列表示中，该表示构建了心脏骤停事件的信息，以分析治疗对患者的影响。作为一个案例研究，分析了最佳/次优心肺复苏（CPR）对Pdes的影响。计算了呈现相同基础心律的CPR前后间隔的平均Pdes。分析了最佳/次优CPR与Pdes增加/减少之间的关系。
VF/VT、PEA、PR和AS的AUC分别为0.80、0.79、0.73和0.61。Pdes量化了事件中每种心律发展到更好状态的概率，并且Pdes的演变与所提供的治疗一致。案例研究表明，对于大多数心律，动态行为中的积极趋势可能与最佳CPR相关，而消极趋势则相反。
提出了一种用于分析复苏期间所有心律的连续ECG波形分析方法。该方法可进一步开发用于CPR技术的回顾性研究，并在未来可能用于实时监测正在接受复苏的患者的存活概率。
