Abstract
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 (Lj) parameter, which is determined in 2n sequence of 2, 4, 8, 16,…, 2048, and the step size (μn), which is automatically randomly identified using variable μn (VSS) optimization. Initially, the algorithm is subjected experimentally to identify the optimal μn range that works with 11 Lj 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 μn values, each of which with all Lj to obtain the output SNRout matrix (4 × 11). The improvement level is estimated and compared with the SNRin prior to the application of the proposed algorithm and after SNRouts. The proposed method achieves high-performance ANC-NLMS algorithm by optimizing VSS when it is close to zero at determining Lj, at which the algorithm shows the capability to separate HSS from LSS. Furthermore, the SNRout of normal LSS starts to improve at Lj of 64 and Lj limit of 1024. The SNRout of abnormal LSS starts from a Lj value of 512 to more than 2048 for all determined μn. Results revealed that the SNRout 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 Lj values. Additionally, the steps of the proposed method and the obtained SNRout may be used to classify LSS by using a computer.
Highlights
Lung sound signals (LSSs) exhibit nonperiodicity and low frequency; these signals contain symptoms of many diseases and interfere with frequency components (50– 2500 Hz) with heart sound signal (HSS) frequency in the range of 20–600 Hz [1]
The improvement in performance level is studied under four values of the optimal variable μn (VSS) and 11 determined Lj values in the following 2n sequence: j 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048. erefore, the performance for one separation is processed 44 times (4μn × 11), that is, the signal-to-noise ratio (SNR) is calculated to obtain 4 × 11 matrix of the output SNRout values. Such combination of the proposed algorithm overcomes the limitations of previous studies in addition to the following: the use of normalized last-mean-square (NLMS) algorithm instead of LMS algorithm because LMS algorithm cannot be adopted with two long signals and the use of adaptive noise canceller (ANC) instead of adaptive line enhancement (ALE). e VSS initially is studied to identify the optimal range that can work with 11 Lj. e level of performance improvement is estimated by comparing the SNR before and after applying the proposed method. e proposed method is carried out and processed using a code program on the MATLAB platform. e proposed method can deal with large data, process repeatedly according to the number of the Lj values, and calculate the SNRout values
Results revealed the ability of the designed ANC-NLMS algorithm to separate HSS from LSS successfully and showed the increasing performance with increasing Lj value. e improved SNR of the normal and abnormal LSSs is achieved at the Lj range of 64–1024 and 512–2048, respectively, at the determined μn. e comparison of SNRin with the obtained matrix of the SNRout aids in exploring the existence of distinguishable characteristics between normal and abnormal LSSs, which can be used in computerized LSS classification
Summary
Lung sound signals (LSSs) exhibit nonperiodicity and low frequency; these signals contain symptoms of many diseases and interfere with frequency components (50– 2500 Hz) with heart sound signal (HSS) frequency in the range of 20–600 Hz [1]. Previous studies have focused on the main parameters of AF, including the filter length (L), constant step size (μn), filter type (such as ALE or ANC), and algorithm (such as NLMS or LMS) to obtain improved AF performance These parameters and combination of techniques have been used with several limitations. Erefore, the performance for one separation is processed 44 times (4μn × 11), that is, the SNR is calculated to obtain 4 × 11 matrix of the output SNRout values Such combination of the proposed algorithm overcomes the limitations of previous studies in addition to the following: the use of NLMS algorithm instead of LMS algorithm because LMS algorithm cannot be adopted with two long signals and the use of ANC instead of ALE. Results revealed the ability of the designed ANC-NLMS algorithm to separate HSS from LSS successfully and showed the increasing performance with increasing Lj value. e improved SNR of the normal and abnormal LSSs is achieved at the Lj range of 64–1024 and 512–2048, respectively, at the determined μn. e comparison of SNRin with the obtained matrix of the SNRout aids in exploring the existence of distinguishable characteristics between normal and abnormal LSSs, which can be used in computerized LSS classification
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