Rodriguez Javier, Voss Andreas, Caminal Pere, Bayes-Genis Antoni, Giraldo Beatriz F
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1332-1335. doi: 10.1109/EMBC.2017.8037078.
Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and T time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.
心脏死亡风险仍然是很大一部分人群面临的重大问题，尤其是老年患者。在本研究中，我们建议使用庞加莱图来表征和分析心血管及心肺系统。共分析了46例心肌病患者和36例健康受试者。左心室射血分数（LVEF）用于将心脏病发作低风险（LR：LVEF > 35%，16例患者）和高风险（HR：LVEF ≤ 35%，30例患者）的患者进行分层。RR、收缩压（SBP）和T时间序列分别从心电图、血压和呼吸流量信号中提取。提取了描述庞加莱方法散点图的参数，这些参数与短期和长期变异性、动态系统的加速和减速以及复杂相关指数有关。使用线性判别分析（LDA）和支持向量机（SVM）分类方法来分析提取参数的结果。结果表明，心脏参数最能区分HR组和LR组，尤其是复杂相关指数（p = 0.009）。分析相互作用时，心脏和呼吸系统标准差之差的关系取得了最佳结果（p = 0.003）。比较HR组和LR组时，应用SVM方法并使用方差分析核，分类准确率为98.12%。使用SVM分类器和拉普拉斯核比较患者与健康人时，准确率为97.01%。庞加莱图的形态引入了能够表征心肺系统动力学的参数。
