A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

Noman Qaid Al-Naggar

doi:10.4236/jbise.2013.69106

Abstract

Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.

Highlights

Biological signals are often noisy and non stationary
This paper proposes a new method of lung sound (LS) filtering which separates heart sounds (HS) and noise from instruments (NI) simultaneously
The effect of changing the above parameters is monitored by power special density (PSD) spectrum of least mean squares (LMS)-adaptive noise cancelling (ANC) output signal e(n) and the graphic view of output signal e(n)

Summary

Introduction

Biological signals are often noisy and non stationary. These factors tremendously complicate analysis of biosignals [1]. Respiratory sounds present noninvasive measures of lung airway conditions [2]. Features of lung sounds (LS) may be contaminated by heart sounds (HS) because lung and heart sounds overlap in terms of time domain and spectral content [3]. All LSs originate from airways during inspiration-expiration-cycles [4]. The LS propagating through lung tissues in the parenchyma can be heard over the chest wall using a sound-transducer. The tissue acts as a frequency filterlike structure whose characteristics vary according to pathological and physiological changes [5]

Methods

Results

Discussion

Conclusion