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Acoustic detection of Korotkoff sounds using non-linear analysis

机译：使用非线性分析对Korotkoff声音进行声学检测

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Coronary artery disease (CAD), a leading cause of death in the United States, has few early diagnostic mechanisms. Acoustic detection of sound associated with turbulent blood flow in coronary arteries has been shown to discriminate normal and abnormal subjects; however, improved performance is required for clinical use. Nonlinear analysis methods sensitive to turbulence could improve acoustic detection of CAD. To assess the ability of these methods to detect turbulent flow, Korotkoff sounds generated by turbulent flow through the brachial artery were used. The nonlinear methods studied include largest Lyapunov and Hurst exponents and Shannon entropy. Data were taken on 3 subjects using hardware developed by SonoMedica (McLean, Virginia). All methods were sensitive to changes in pressure applied to the brachial artery. The Lyapunov exponents decreased with increasing pressure. Entropy and Hurst exponent methods showed the opposite trend. In particular, the entropy measurements showed a 50% decrease from high to low pressure. All Lyapunov exponents were positive, indicating the presence of chaotic components in the signal. Hurst exponents near 1.0 are indicative of long term persistence in measured high pressure signals. These results indicate that nonlinear methods may be suitable for detecting CAD using heart sound recordings from diseased subjects.
机译：冠状动脉疾病（CAD）是美国的主要死亡原因，几乎没有早期诊断机制。声音检测显示与冠状动脉湍流相关的声音可以区分正常和异常受试者。但是，临床使用需要提高性能。对湍流敏感的非线性分析方法可以改善对CAD的声学检测。为了评估这些方法检测湍流的能力，使用了通过肱动脉的湍流产生的Korotkoff声音。研究的非线性方法包括最大的Lyapunov和Hurst指数以及Shannon熵。使用SonoMedica（弗吉尼亚州麦克莱恩）开发的硬件对3个受试者进行了数据采集。所有方法均对施加于肱动脉的压力变化敏感。随着压力的增加，李雅普诺夫指数下降。熵和赫斯特指数法显示出相反的趋势。特别地，熵测量显示从高压到低压降低了50％。所有Lyapunov指数均为阳性，表明信号中存在混沌成分。接近1.0的赫斯特指数表明在测得的高压信号中存在长期持久性。这些结果表明，非线性方法可能适用于使用来自患病对象的心音记录来检测CAD。

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