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Heartbeat detection with Doppler radar based on spectrogram

机译:基于频谱图的多普勒雷达心跳检测

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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.
机译:代表心跳峰到峰间隔的R-R间隔的变化表示精神状况。多普勒雷达可以以较少的对象负担来捕获心跳的信息,从而减轻了对象的压力。但是,使用多普勒雷达进行非接触式心跳检测很容易受到呼吸和身体运动的影响。在本文中,我们提出了一种基于频谱图的R-R间隔检测算法。我们的算法从可能会响应频谱图中心跳的频率中确定包含心跳分量的频带。我们对频带内心跳引起的频率振幅进行积分,以消除由呼吸和身体小动作引起的噪声。然后,我们检测到与心跳相对应的频率的积分振幅中的峰值。通常,R-R间隔在两个相邻间隔之间不会很大地变化。因此,我们为检测到的两个相邻峰峰值间隔之差设置了一个阈值。如果通过阈值判断峰峰间隔与R-R间隔不对应,我们将删除相应的峰,并根据相邻的峰峰间隔对峰进行插值。通过实验表明,当对象静止不动时,与以前的算法相比,我们的算法提高了R-R间隔的检测精度,与以前的算法相比,该算法能够实现更好的检测精度。而且,我们确认,检测精度的提高对于准确地计算应力指数是有效的。

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