School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China.
Key Lab for Hydropower Transients of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, 8 East Lake South Road, Wuhan 430072, China.
Sensors (Basel). 2020 Mar 13;20(6):1596. doi: 10.3390/s20061596.
This paper presents a novel adaptive recursive least squares filter (ARLSF) for motion artifact removal in the field of seismocardiography (SCG). This algorithm was tested with a consumer-grade accelerometer. This accelerometer was placed on the chest wall of 16 subjects whose ages ranged from 24 to 35 years. We recorded the SCG signal and the standard electrocardiogram (ECG) lead I signal by placing one electrode on the right arm (RA) and another on the left arm (LA) of the subjects. These subjects were asked to perform standing and walking movements on a treadmill. ARLSF was developed in MATLAB to process the collected SCG and ECG signals simultaneously. The SCG peaks and heart rate signals were extracted from the output of ARLSF. The results indicate a heartbeat detection accuracy of up to 98%. The heart rates estimated from SCG and ECG are similar under both standing and walking conditions. This observation shows that the proposed ARLSF could be an effective method to remove motion artifact from recorded SCG signals.
本文提出了一种新颖的自适应递归最小二乘滤波器（ARLSF），用于去除地震心动图（SCG）领域中的运动伪影。该算法使用消费级加速度计进行了测试。将一个电极放置在受试者的右臂（RA）上，另一个电极放置在左臂（LA）上，以记录 SCG 信号和标准心电图（ECG）导联 I 信号。这些受试者被要求在跑步机上进行站立和行走运动。使用 MATLAB 开发了 ARLSF 来同时处理采集到的 SCG 和 ECG 信号。从 ARLSF 的输出中提取了 SCG 峰值和心率信号。结果表明，心跳检测准确率高达 98%。在站立和行走两种情况下，从 SCG 和 ECG 估计的心率相似。这一观察结果表明，所提出的 ARLSF 可能是一种从记录的 SCG 信号中去除运动伪影的有效方法。
