Abstract
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).
Highlights
The conventional stethoscope based auscultation technique is a cost-effective and non-invasive diagnostic procedure
The efficiency of the proposed method is measured by evaluating the results in terms of false positive rate (FPR), false negative rate (FNR), accuracy (ACC), detection error rate (DER), and execution time (ET), and compared with the Singular Spectrum Analysis (SSA) method
The FNR, ACC, and DER of the proposed method are significantly better than the SSA method for various types of mixture (Tables 2, 3, 4, 5,) and real lung sound (LS) data (Table 6) at different flow rates
Summary
The conventional stethoscope based auscultation technique is a cost-effective and non-invasive diagnostic procedure. Gadheri et al has shown the superiority of SSA method over ENT technique in (Ghaderi et al 2011) All these techniques highlight their performances for normal lung sound signals at low and medium flow rate but not at high flow rate. The rising edge gives the positive gradient values and falling edge gives negative gradient values at each point over the envelope These peaks are detected through the following steps: Step 1: Smoothening of the envelope: The Hilbert envelope EH (n) of the signal is not smooth because of the presence of lung sound components. The high, medium and low flow rates mixed signal are synthesized by varying the amplitude ratios of the heart and lung sound signals. The MATLAB (R2008a, The Mathworks, Inc., Natick, MA) tool is used for conducting the all experiments
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