Zhu Peibin, Feng Lei, Yu Kaimin, Zhang Yuanfang, Dai Meiling, Chen Wen, Hao Jianzhong
School of Ocean Information Engineering, Jimei University, Xiamen 361021, China.
School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, China.
Sensors (Basel). 2025 Mar 11;25(6):1743. doi: 10.3390/s25061743.
Achieving both accurate and real-time monitoring heartbeat signals by non-invasive sensing techniques is challenging due to various noise interferences. In this paper, we propose an enhanced discrete wavelet transform (DWT) method that incorporates objective denoising quality assessment metrics to determine accurate thresholds and adaptive threshold functions. Our approach begins by denoising ECG signals from various databases, introducing several types of typical noise, including additive white Gaussian (AWG) noise, baseline wandering noise, electrode motion noise, and muscle artifacts. The results show that for Gaussian white noise denoising, the enhanced DWT can achieve 1-5 dB SNR improvement compared to the traditional DWT method, while for real noise denoising, our proposed method improves the SNR tens or even hundreds of times that of the state-of-the-art denoising techniques. Furthermore, we validate the effectiveness of the enhanced DWT method by visualizing and comparing the denoising results of heartbeat signals monitored by fiber-optic micro-vibration sensors against those obtained using other denoising methods. The improved DWT enhances the quality of heartbeat signals from non-invasive sensors, thereby increasing the accuracy of cardiovascular disease diagnosis.
由于存在各种噪声干扰，通过非侵入式传感技术实现对心跳信号的准确实时监测具有挑战性。在本文中，我们提出了一种增强型离散小波变换（DWT）方法，该方法结合了客观的去噪质量评估指标来确定准确的阈值和自适应阈值函数。我们的方法首先对来自各种数据库的心电图信号进行去噪，引入几种典型噪声，包括加性高斯白（AWG）噪声、基线漂移噪声、电极运动噪声和肌肉伪迹。结果表明，对于高斯白噪声去噪，增强型DWT与传统DWT方法相比，可实现1 - 5 dB的信噪比提升，而对于实际噪声去噪，我们提出的方法将信噪比提高到了现有最先进去噪技术的数十倍甚至数百倍。此外，我们通过可视化和比较光纤微振动传感器监测的心跳信号的去噪结果与使用其他去噪方法获得的结果，验证了增强型DWT方法的有效性。改进后的DWT提高了非侵入式传感器心跳信号的质量，从而提高了心血管疾病诊断的准确性。
