Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4586-4589. doi: 10.1109/EMBC.2017.8037877.
Chronic heart failure (CHF) is a cardiac condition caused by various of cardiac diseases in the end stage. This paper employed the linear and nonlinear approaches to analyze the heart sound (HS) signals from the patients with CHF. The linear approaches include the time and frequency domain analysis. The nonlinear parameters include largest Lyapunov exponent, correlation dimension, sample entropy and the width of multifractal spectrum, which describe the chaos, fractal characteristics and complexity of the HS signals. Statistical test and receiver operating characteristic (ROC) curve analysis have been applied to the characteristic parameters extracted from the HS signals of the healthy subjects and CHF patients. The results show that the statistically significant differences of linear and nonlinear features between the healthy and CHF groups can be observed. Compared to the healthy people, the cardiac mechanical activity of the patients with CHF has a decreased chaotic characteristic, complexity and randomness, and it indicates the HS features could be the measure to distinguish the CHF patients from the healthy subjects. Hence, our study suggests the proposed features could be as supplementary indexes or efficient clues for the diagnosis of CHF.
慢性心力衰竭（CHF）是各种心脏疾病终末期导致的一种心脏病症。本文采用线性和非线性方法分析慢性心力衰竭患者的心音（HS）信号。线性方法包括时域和频域分析。非线性参数包括最大Lyapunov指数、关联维数、样本熵和多重分形谱宽度，这些参数描述了心音信号的混沌、分形特征和复杂性。对从健康受试者和慢性心力衰竭患者的心音信号中提取的特征参数进行了统计检验和受试者工作特征（ROC）曲线分析。结果表明，健康组和慢性心力衰竭组之间的线性和非线性特征存在统计学上的显著差异。与健康人相比，慢性心力衰竭患者的心脏机械活动的混沌特征、复杂性和随机性降低，这表明心音特征可作为区分慢性心力衰竭患者和健康受试者的指标。因此，我们的研究表明，所提出的特征可作为慢性心力衰竭诊断的补充指标或有效线索。
