Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton, MI 49931, USA.
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK.
Sensors (Basel). 2019 Apr 4;19(7):1617. doi: 10.3390/s19071617.
Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.
心电图（ECG）感应是诊断心血管疾病的重要应用。最近，在可穿戴电子技术的新兴技术的推动下，大量的可穿戴 ECG 传感器被开发出来，然而这也给这些可穿戴 ECG 传感器的 ECG 信号带来了额外的噪声污染源。在本文中，我们提出了一种新的基于离散曲率估计的低失真自适应 Savitzky-Golay（LDASG）滤波方法，用于 ECG 去噪，该方法比现有的 ECG 去噪方法具有更好的性能。标准的 Savitzky-Golay（SG）滤波器在数据平滑方面具有显著的性能。然而，它缺乏对信号变化的适应性，因此对于 ECG 等变化较大的信号往往会导致信号失真。在我们的方法中，离散曲率估计被自适应地用于表示信号变化，以减轻信号失真。通过自适应地为每个数据样本设计适当的 SG 滤波器，根据离散曲率，该方法仍然保留了 SG 滤波器的固有优势，即具有出色的数据平滑性能，并进一步解决了具有低信号失真的高信号变化的去噪挑战。在我们的实验中，我们将我们的方法与基于 EMD 小波的方法和非局部均值（NLM）去噪方法在噪声消除和信号失真减少方面的性能进行了比较。特别是对于信号失真减少，与 EMD 小波相比，我们的方法在均方误差（MSE）方面降低了 33.33%，与 NLM 相比降低了 50%，在峰值信噪比（PRD）方面降低了 18.25%，与 NLM 相比降低了 25.24%。我们的方法在 ECG 去噪的广泛应用中具有很高的潜力和可行性，无论是在临床应用还是消费电子领域。
