Key Laboratory of High Speed Circuit Design and EMC of Ministry of Education, School of Electronic EngineeringXidian University Xi'an Shaanxi 710071 China.
School of Electronic Engineering and Computer ScienceQueen Mary University of London E1 4NS London U.K.
IEEE J Transl Eng Health Med. 2024 Jul 29;12:558-568. doi: 10.1109/JTEHM.2024.3434460. eCollection 2024.
Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.
生命体征是评估患者健康状况的重要指标。信道状态信息 (CSI) 可以非接触方式感知胸壁因心肺活动引起的位移。由于杂波、直流分量和呼吸谐波的影响，很难检测到可靠的心跳信号。针对这一问题，本文提出了一种使用软件定义无线电 (SDR) 同时提取呼吸和心跳信号的稳健新方法。具体来说，我们对信号进行建模和分析，并提出基于奇异值分解 (SVD) 的杂波抑制方法来增强生命体征信号。通过圆拟合方法估计和补偿直流分量。然后，通过改进的变分模态分解 (VMD) 获得心跳信号和呼吸信号。实验结果表明，所提出的方法可以从滤波信号中准确地分离呼吸信号和心跳信号。Bland-Altman 分析表明，所提出的系统与医疗传感器吻合良好。此外，该系统可以在 0.5m 内准确测量心率变异性 (HRV)。总之，我们的系统可以作为传统接触式医疗传感器的首选非接触式替代方案，为先进的以患者为中心的医疗保健解决方案提供支持。
