基于谱图的多普勒雷达心跳检测

Eriko Mogi, T. Ohtsuki
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引用次数: 22

摘要

R-R间隔的可变性表示心跳的峰值到峰值间隔,表明精神状况。多普勒雷达可以捕获心跳信息,对被测者的负担较小,从而减轻被测者的压力。然而,使用多普勒雷达的非接触式心跳检测容易受到呼吸和身体运动的影响。本文提出了一种基于谱图的R-R区间检测算法。我们的算法从频谱图中可能响应心跳的频率中确定包含心跳成分的频带。我们在频带内整合心跳频率的振幅,以消除呼吸和微小的身体运动引起的噪音。然后,我们在与心跳相对应的频率的综合振幅中检测峰值。一般来说,R-R区间在两个相邻区间之间变化不大。因此,我们为检测到的两个相邻峰到峰间隔的差设置一个阈值。如果通过阈值判断峰到峰间隔不对应于R-R间隔,则移除相应的峰,并根据相邻的峰到峰间隔插入一个峰。通过实验,我们发现当被试静止不动时,我们的算法比我们之前的算法提高了R-R区间的检测精度,能够达到比其他现有算法更好的检测精度。进一步验证了检测精度的提高对于准确计算应力指数是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heartbeat detection with Doppler radar based on spectrogram
A variability of R-R intervals that represent the peak-to-peak intervals of the heartbeats indicates the mental condition. Doppler radar can capture the information of heartbeats with less burden on subjects, which leads to less stress of subjects. However, non-contact heartbeat detection using Doppler radar is easily affected by respiration and body movements. In this paper, we propose a detection algorithm of R-R intervals based on the spectrogram. Our algorithm determines the frequency bands containing the heartbeats components from the frequencies that might respond to heartbeats in the spectrogram. We integrate the amplitudes of frequencies due to heartbeats within the frequency band to eliminate the noise caused by respiration and small body movements. Then, we detect peaks in the integrated amplitudes of frequencies corresponding to heartbeats. In general, the R-R intervals do not largely change between two adjacent intervals. Thus, we set a threshold to the difference of two adjacent peak-to-peak intervals that are detected. If the peak-to-peak interval is judged not corresponding to an R-R interval by the threshold, we remove the corresponding peak and interpolate a peak based on the adjacent peak-to-peak intervals. Through experiments, we show that when the subjects were sitting still, our algorithm improved the detection accuracy of the R-R intervals compared with our previous algorithm that was able to achieve a better detection accuracy than the other existing algorithms. Moreover, we confirmed that the improvement of the detection accuracy is effective to accurately calculate the stress index.
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