Cui Guangyu, Wang Yujie, Zhang Xinyi, Li Jiale, Liu Xinfeng, Li Bijie, Wang Jiayi, Zhang Quan
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2025 Sep 8;25(17):5607. doi: 10.3390/s25175607.
Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects' micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end.
心率变异性（HRV）定量表征逐搏间期的波动，是心血管和自主神经系统健康的关键指标。非接触式方法无需受试者接触的固有能力有效减轻了用户负担，并便于进行可扩展的长期监测，因此在非接触式HRV传感方面引起了相当大的研究兴趣。在本研究中，我们提出了一种利用调频连续波毫米波雷达提取HRV的新算法。首先，我们开发了一个免校准的三维目标定位模块，通过整合数字波束形成、动目标显示滤波和DBSCAN（基于密度的带有噪声的空间聚类应用）聚类技术来捕获受试者的微动信号。其次，我们为呼吸和心脏振动建立了基于相位的独立数学模型，以实现系统的信号分离。第三，我们实现了基于拉格朗日乘子的二阶谱稀疏分离算法，从而在存在呼吸运动和噪声的情况下实现稳健的心跳提取。通过对恢复的心脏信号进行峰值检测来识别心跳事件，随后从中导出逐搏间期和HRV指标。与现有算法和传统滤波器组方法相比，该方法的平均IBI（逐搏间期）估计误差降低了50%以上，同时在所有测试场景中保持了一致的准确性。然而，应该注意的是，该方法目前仅适用于受试者运动受限的场景，并且已经在离线模式下得到验证，但在文末对这两个问题进行了讨论。
