Heart Sound Interference Cancellation from Lung Sound Using Dynamic Neighborhood Learning-Particle Swarm Optimizer Based Optimal Recursive Least Square Algorithm

Abstract

Cancellation of acoustic interferences from lung sound recordings is a challenging task. Lung sound signals (LSS) provide critical analysis of lung functions. Lung diseases are diagnosed with noiseless LSS. Heart sound signal (HSS) is one of the main acoustic noises in LSS recordings. In this paper, a recursive least square (RLS) based adaptive noise cancellation is proposed to reduce HSS from LSS. In RLS, the forgetting factor is the important parameter which determines the performance of filter. Choosing optimal forgetting factor (λ) is the vital step in RLS operation. An enhanced particle swarm algorithm is used to find the optimal forgetting factor. Bronchial, tracheal and vesicular LSS are mixed with HSS to test the algorithm. The results are assessed with correlation coefficient between the uncorrupted and the interference cancelled LSS by the proposed optimal filter. The power spectral density plots are used to measure the accuracy of the proposed optimal RLS algorithm.