Department of Mechanical Engineering, 161 Louis Pasteur, University of Ottawa, K1N 6N5, Ottawa, Ontario, Canada.
Biomed Eng Online. 2011 Jan 26;10:10. doi: 10.1186/1475-925X-10-10.
Heart signals represent an important way to evaluate cardiovascular function and often what is desired is to quantify the level of some signal of interest against the louder backdrop of the beating of the heart itself. An example of this type of application is the quantification of cavitation in mechanical heart valve patients.
An algorithm is presented for the quantification of high-frequency, non-deterministic events such as cavitation from recorded signals. A closed-form mathematical analysis of the algorithm investigates its capabilities. The algorithm is implemented on real heart signals to investigate usability and implementation issues. Improvements are suggested to the base algorithm including aligning heart sounds, and the implementation of the Short-Time Fourier Transform to study the time evolution of the energy in the signal.
The improvements result in better heart beat alignment and better detection and measurement of the random events in the heart signals, so that they may provide a method to quantify nondeterministic events in heart signals. The use of the Short-Time Fourier Transform allows the examination of the random events in both time and frequency allowing for further investigation and interpretation of the signal.
The presented algorithm does allow for the quantification of nondeterministic events but proper care in signal acquisition and processing must be taken to obtain meaningful results.
心脏信号是评估心血管功能的重要方式，通常需要将感兴趣的信号水平与心脏自身跳动的较大背景噪声进行定量比较。这种应用的一个例子是对机械心脏瓣膜患者的空化现象进行定量分析。
提出了一种从记录信号中定量分析高频、非确定性事件（如空化）的算法。通过对算法的封闭形式数学分析来研究其性能。该算法已应用于真实的心脏信号，以研究可用性和实现问题。对基本算法进行了改进，包括对齐心音，并实现了短时傅里叶变换，以研究信号中能量的时变。
改进后的算法提高了心搏对齐的准确性，更好地检测和测量了心脏信号中的随机事件，从而为量化心脏信号中的非确定性事件提供了一种方法。使用短时傅里叶变换可以同时在时间和频率上研究随机事件，从而进一步研究和解释信号。
所提出的算法确实可以对非确定性事件进行定量分析，但为了获得有意义的结果，必须在信号采集和处理方面加以适当的注意。
