Ye Chen, Toyoda Kentaroh, Ohtsuki Tomoaki
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-6. doi: 10.1109/EMBC.2018.8512544.
Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.
最近，一种名为零吸引符号最小均方（ZA-SLMS）的稀疏自适应算法已被证明能够通过多普勒雷达信号稳健地重建心跳频谱。然而，由于在不同身体运动下噪声强度明显不同，在ZA-SLMS算法中应用平衡梯度校正和稀疏惩罚的恒定正则化参数（REPA）并不能总是准确地获取稀疏心跳频谱。本文通过引入自适应REPA（AREPA）提出了一种改进的ZA-SLMS算法，其中基于雷达数据的标准差调整稀疏惩罚的比例。此外，考虑到在短时间内时间窗长度变化时与心率（HR）相关的频谱峰值位置稳定这一事实，为提高心跳检测的稳定性，在改进的ZA-SLMS算法中进一步引入了时间窗变化（TWV）技术。针对五名受试者的实验结果验证了我们的方案比标准ZA-SLMS算法能可靠地降低心率估计误差。
