Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada.
IEEE Trans Biomed Eng. 2013 Feb;60(2):417-26. doi: 10.1109/TBME.2012.2227318. Epub 2012 Nov 15.
Although clinical percussion remains one of the most widespread traditional noninvasive methods for diagnosing pulmonary disease, the available analysis of physical characteristics of the percussion sound using modern signal processing techniques is still quite limited. The majority of existing literature on the subject reports either time-domain or spectral analysis methods. However, Fourier analysis, which represents the signal as a sum of infinite periodic harmonics, is not naturally suited for decomposition of short and aperiodic percussion signals. Broadening of the spectral peaks due to damping leads to their overlapping and masking of the lower amplitude peaks, which could be important for the fine-level signal classification. In this study, an attempt is made to automatically decompose percussion signals into a sum of exponentially damped harmonics, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. The damped harmonic decomposition of percussion signals recorded on healthy volunteers in clinical setting is performed using the matrix pencil method, which proves to be quite robust in the presence of noise and well suited for the task.
虽然临床叩诊仍然是诊断肺部疾病最广泛的传统非侵入性方法之一，但使用现代信号处理技术对叩诊音的物理特征进行的可用分析仍然相当有限。关于这个主题的大多数现有文献要么报告了时域分析方法，要么报告了频域分析方法。然而，傅里叶分析将信号表示为无限个周期谐波的和，并不自然适合于分解短暂和非周期性的叩诊信号。由于阻尼导致频谱峰展宽，从而导致它们重叠并掩盖了较低幅度的峰，这对于精细水平的信号分类可能很重要。在这项研究中，尝试将叩诊信号自动分解为指数衰减谐波的和，在这种情况下，它们比傅里叶谐波形成更自然的基础，从而允许在参数空间中更稳健地表示信号。使用矩阵束方法对临床环境中健康志愿者记录的叩诊信号进行了阻尼谐波分解，该方法在存在噪声的情况下表现出很强的稳健性，非常适合该任务。
