Rodriguez Javier, Schulz Steffen, Giraldo Beatriz F, Voss Andreas
Institute for Bioengineering of Catalonia, The Barcelona Institute of Science and Technology, Barcelona, Spain.
Automatic Control Department (ESAII), Barcelona East School of Engineering (EEBE), Universitat Politècnica de Catalunya, Barcelona, Spain.
Front Physiol. 2019 Jul 9;10:841. doi: 10.3389/fphys.2019.00841. eCollection 2019.
Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDC) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDC) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDC patients presented lowered HRV and increased BPV compared to both IDC patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDC vs. IDC model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.
心血管疾病是最常见的死亡原因之一；然而，早期发现心脏性猝死（SCD）高危患者仍然是一个问题。本研究的目的是基于心率变异性（HRV）和血压变异性（BPV）分析来分析心血管耦合，以便为特发性扩张型心肌病患者（IDC）引入无创风险分层的新指标。对91例IDC患者和49例健康受试者（CON）记录了高分辨率心电图（ECG）和连续无创血压（BP）信号。根据SCD风险将患者分层：如果两年后受试者死亡或出现危及生命的并发症，则为高风险（IDC）；如果在此期间受试者保持稳定，则为低风险（IDC）。从ECG和BP信号、逐搏间期以及收缩压和舒张压中提取数值，并使用分段庞加莱图分析（SPPA）、高分辨率联合符号动力学（HRJSD）和归一化短时偏相干方法进行分析。建立支持向量机（SVM）模型以根据SCD风险对这些患者进行分类。与低风险IDC患者和对照受试者相比，高风险IDC患者的HRV降低且BPV升高，这表明其迷走神经活动减少且交感神经活动得到代偿。与低风险患者相比，高风险患者的心脏收缩期和舒张期耦合强度均更强。心脏收缩期耦合分析显示，随着风险增加，收缩压对心率的影响减弱。SVM IDC与IDC模型的准确率达到98.9%，曲线下面积（AUC）为0.96。IDC组和CON组的准确率和AUC分别为93.6%和0.94。为了模拟受试者原始状态未知的情况，构建了一个融合上述模型的级联模型，其准确率达到94.4%。总之，本研究基于耦合分析以及非线性HRV和BPV的新指标，为IDC患者引入了一种新的SCD风险分层方法。我们揭示了这类患者自主神经调节中的一些复杂相互作用。
