Mondal Ashok, Bhattacharya Parthasarathi, Saha Goutam
Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721 302 India.
Springerplus. 2013 Oct 5;2:512. doi: 10.1186/2193-1801-2-512. eCollection 2013.
The primary problem with lung sound (LS) analysis is the interference of heart sound (HS) which tends to mask important LS features. The effect of heart sound is more at medium and high flow rate than that of low flow rate. Moreover, pathological HS obscures LS in a higher degree than normal HS. To get over this problem, several HS reduction techniques have been developed. An important preprocessing step in HS reduction is localization of HS components. In this paper, a new HS localization algorithm is proposed which is based on Hilbert transform (HT) and Heron's formula. In the proposed method, the HS included segment is differentiated from the HS excluded segment by comparing their area with an adaptive threshold. The area of a HS component is calculated from the Hilbert envelope using Heron's triangular formula. The method is tested on real recorded and simulated HS corrupted LS signals. All the experiments are conducted under low, medium and high breathing flow rates. The proposed method shows a better performance than the comparative Singular Spectrum Analysis (SSA) based method in terms of accuracy (ACC), detection error rate (DER), false negative rate (FNR), and execution time (ET).
肺音（LS）分析的主要问题是心音（HS）的干扰，心音往往会掩盖重要的肺音特征。心音在中高流速时的影响比低流速时更大。此外，病理性心音比正常心音更能掩盖肺音。为了解决这个问题，已经开发了几种心音降低技术。心音降低的一个重要预处理步骤是心音成分的定位。本文提出了一种基于希尔伯特变换（HT）和海伦公式的新的心音定位算法。在所提出的方法中，通过将包含心音的段与排除心音的段的面积与自适应阈值进行比较，来区分它们。使用海伦三角形公式从希尔伯特包络计算心音成分的面积。该方法在真实记录和模拟的受心音干扰的肺音信号上进行了测试。所有实验均在低、中、高呼吸流速下进行。在所提出的方法在准确率（ACC）、检测错误率（DER）、假阴性率（FNR）和执行时间（ET）方面比基于奇异谱分析（SSA）的对比方法表现出更好的性能。
