Performance Analysis: Preprocessing of Respiratory Lung Sounds

Abstract

Computerized lung sound analysis for automatic detection and classification of adventitious lung sounds is an emerging technique for the diagnosis of pulmonary diseases. Automated analysis of lung sound signals involves acquisition of clean lung sounds and identification of key factors present in the signal to aid the physician in recognizing the category of adventitious lung sounds. There is a possibility that the acquired lung sounds may be corrupted with interferences such as heart sound, artifacts due to improper mounting of sensor and power line interference. It also depends upon the environment in which the signals are recorded. Therefore preprocessing of the signal plays the key role in diagnosis and interpretation of lung sound. In this work the lung sounds are preprocessed using Recursive Least Mean Square (RLS), Least Mean Square (LMS), Square root Recursive Least Mean Square (SRLS), Discrete Wavelet Transform (DWT) and Total Variation De-noising (TVD) methods. The performance metrics Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) and Cross Correlation (CC) are computed for the evaluation of RLS, LMS, SRLS, DWT and TVD. It is observed from the results that the DWT performs better compared to RLS, LMS, SRLS and TVD in removing the artifacts from the lung sounds.