基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法

Adaptive Wavelet Scale Selection-based Method for Separating Respiration and Heartbeat in Bio-radars

当使用生物雷达进行生命体征参数提取时,由于心脏跳动产生的位移形变很小,回波较为微弱,而呼吸带动的胸腔起伏回波强度较大,基于简单傅里叶变换的周期性信息检测,往往无法有效提取心跳信号。采用小波变换的方法可以较好地分离出含有心跳、呼吸运动信息的信号分量,但小波尺度的选择对于不同的场景存在细微差异,影响到了分离的效果。针对这一问题,该文采用Morlet二进小波变换,提出了一种基于信噪比阈值定标的自适应小波尺度选择方法,有效解决了不同场景的呼吸心跳分离问题。最后通过实测结果验证了算法的准确性和可行性。

Extracting periodic heartbeat signals based on the traditional Fourier transform using a noncontact bio-radar is difficult because chest displacements caused by the heart are much smaller than those caused by respiration. Normally, they can be separated using the continuous wavelet transform; however, the miniscule difference of wavelet scale selection under different conditions may influence the separation performance to some extent. To solve this problem, this study proposes a method based on signal-to-noise ratio calibration to adaptively select the Morletdyadic wavelet scales and then separate the heartbeat signal from the respiration one using the selected scales, which can be applied to detect vital signs of different conditions. The experimental results have exhibited the accuracy and feasibility of the proposed method.