Iwata Yuki, Ishibashi Koichiro, Sun Guanghao, Luu Manh Ha, Han Trong Thanh, Nguyen Linh Trung, Do Trong Tuan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:477-480. doi: 10.1109/EMBC44109.2020.9175441.
The continuous-wave Doppler radar measures the movement of a chest surface including of cardiac and breathing signals and the body movement. The challenges associated with extracting cardiac information in the presence of respiration and body movement have not been addressed thus far. This paper presents a novel method based on the windowed-singular spectrum analysis (WSSA) for solving this issue. The algorithm consists of two processes: signal decomposition via WSSA followed by the reconstruction of decomposed heartbeat signals through convolution. An experiment was conducted to collect chest signals in 212 people by Doppler radar. In order to confirm the effect of reducing the large noise by the proposed method, we evaluated 136 signals that were considered to contain respiration body movements from the collected signals. When comparing to the performance of a band-pass filter, the proposed analysis achieves improved beat count accuracy. The results indicate its applicability to contactless heartbeat estimation under involving respiration and body movements.
连续波多普勒雷达测量胸部表面的运动，包括心脏和呼吸信号以及身体运动。迄今为止，在存在呼吸和身体运动的情况下提取心脏信息所面临的挑战尚未得到解决。本文提出了一种基于加窗奇异谱分析（WSSA）的新方法来解决这个问题。该算法由两个过程组成：通过WSSA进行信号分解，然后通过卷积重建分解后的心跳信号。通过多普勒雷达对212人进行了收集胸部信号的实验。为了确认所提方法降低大噪声的效果，我们从收集的信号中评估了136个被认为包含呼吸身体运动的信号。与带通滤波器的性能相比，所提分析实现了更高的心跳计数精度。结果表明其适用于在存在呼吸和身体运动的情况下进行非接触式心跳估计。
