Effects of the physiological parameters on the signal-to-noise ratio of single myoelectric channel

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 2ⁿ 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.