Performance of Adaptive Noise Cancellation with Normalized Last-Mean-Square Based on the Signal-to-Noise Ratio of Lung and Heart Sound Separation

The adaptive algorithm satisfies the present needs on technology for diagnosis biosignals as lung sound signals (LSSs) and accurate techniques for the separation of heart sound signals (HSSs) and other background noise from LSS. This study investigates an improved adaptive noise cancellation (ANC) based on normalized last-mean-square (NLMS) algorithm. The parameters of ANC-NLMS algorithm are the filter length (L(j)) parameter, which is determined in 2(n) sequence of 2, 4, 8, 16,…, 2048, and the step size (μ(n)), which is automatically randomly identified using variable μ(n) (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μ(n) range that works with 11 L(j) values as a specific case. This case is used to study the improved performance of the proposed method based on the signal-to-noise ratio and mean square error. Moreover, the performance is evaluated four times for four μ(n) values, each of which with all L(j) to obtain the output SNR(out) matrix (4 × 11). The improvement level is estimated and compared with the SNR(in) prior to the application of the proposed algorithm and after SNR(outs). The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining L(j), at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNR(out) of normal LSS starts to improve at L(j) of 64 and L(j) limit of 1024. The SNR(out) of abnormal LSS starts from a L(j) value of 512 to more than 2048 for all determined μ(n). Results revealed that the SNR(out) of the abnormal LSS is small (negative value), whereas that in the normal LSS is large (reaches a positive value). Finally, the designed ANC-NLMS algorithm can separate HSS from LSS. This algorithm can also achieve a good performance by optimizing VSS at the determined 11 L(j) values. Additionally, the steps of the proposed method and the obtained SNR(out) may be used to classify LSS by using a computer.