Abstract:
Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.
摘要:
可穿戴感知已成为心脏健康监测的重要方法,和心脏地震描记术(SCG)正在成为该领域的一项有前途的技术。然而,SCG的适用性受到运动伪影的阻碍,包括那些在实践中遇到的最强的来源是行走。这阻碍了SCG到临床设置的转换。因此,我们研究了在存在运动伪影的情况下增强SCG信号质量的技术。为了模拟流动的录音,我们用真实的行走振动噪声破坏了一个干净的SCG数据集。我们使用几种经验模式分解方法和最大重叠离散小波变换(MODWT)对信号进行分解。通过结合MODWT,时频掩蔽,和非负矩阵分解,我们开发了一种新颖的算法,该算法利用垂直轴加速度计来减少背腹侧SCG的步行振动。使用心率估计验证了我们方法的准确性和适用性。我们使用了交互式选择方法来提高估计精度。减少运动伪影噪声的最佳分解方法是MODWT。我们的算法在-15dB信噪比(SNR)下将心率估计从0.1r平方提高到0.8r平方。我们的方法将SCG信号中的运动伪影减少到高达-19dB的SNR,而无需心电图(ECG)的任何外部辅助。这种独立的解决方案直接适用于SCG在日常生活中的使用,作为临床环境中其他可穿戴设备的内容丰富的替代品,和其他连续监测方案。在噪声水平较高的应用中,可以合并ECG以进一步增强SCG并扩展其可用范围。这项工作解决了运动伪影带来的挑战,使SCG能够在更困难的情况下提供可靠的心血管见解,从而促进日常生活和临床中的可穿戴监测。