Department of Electronics and Communication Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Kanchipuram, Chennai, Tamil Nadu, India.
Department of Electrical and Electronics Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Kanchipuram, Chennai, Tamil Nadu, India.
J Healthc Eng. 2022 Apr 30;2022:3039624. doi: 10.1155/2022/3039624. eCollection 2022.
Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8-10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75-80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.
心音图（PCG）是对心脏信号的图形记录，用于分析确定心脏的机械功能。在 PCG 信号的记录中，遇到的主要问题是周围噪声信号的干扰。受噪声干扰的信号无法进行分析和用于进一步的处理。因此，需要对这些信号进行去噪处理，然后再用于进一步处理。自适应噪声消除器最适合用于信号去噪应用，可以有效地恢复受干扰的 PCG 信号。本文提出了一种使用 Sign Error LMS 算法的最优自适应滤波器结构，该结构可以高精度地估计无噪声信号。在提出的滤波器结构中，将噪声信号通过多级级联自适应滤波器结构。级联的级数和每个级的步长自动调整。将提出的可变级联 Sign Error LMS（SELMS）自适应滤波器模型用于对 SUFHS 数据库中获取的胎儿 PCG 信号进行去噪，该信号受到不同输入 SNR 水平的高斯噪声和彩色粉红噪声的干扰。还针对存在高斯噪声的病理性 PCG 信号对所提出的滤波器模型进行了测试。仿真结果表明，与现有的级联 LMS 滤波器模型相比，所提出的滤波器模型在高斯噪声环境下的 SNR 值提高了 8-10 dB，在有色噪声环境下的 SNR 值提高了 2-3 dB。在高斯噪声的情况下，MSE 值提高了 75-80%。此外，去噪后干净信号与其估计之间的相关性大于 0.99。在高斯噪声环境中，PSNR 值提高了 7 dB，在粉红噪声环境中提高了 1-2 dB。在提出的滤波器模型中使用 SELMS 自适应滤波器的优点是，它提供了具有高精度的自适应噪声消除器的具有成本效益的硬件实现。
