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首页> 外文期刊>American Journal of Biomedical Engineering >An Efficient Algorithm for Remote Detection of Simulated Heart Rate Using Ultra-Wide Band Signals
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An Efficient Algorithm for Remote Detection of Simulated Heart Rate Using Ultra-Wide Band Signals

机译：一种利用超宽带信号远程检测模拟心率的有效算法

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摘要

Ultra-wideband (UWB) signals have become attractive because of their particular advantage of having a narrow pulse width, which makes them suitable for non-invasive remote sensing of vital signals. In this paper, we present an efficient algorithm based on power spectral method applied on UWB signals for non-invasive monitoring and measurement of simulated heart rates. The objective of this study is to evaluate practical algorithms for wireless detection of the human heart rate using UWB signals in a noisy environment. To perform this, the heart rate is first simulated by our designed moving phantom with variable speed and range of motion. Then, these data were entered in the proposed simulation framework including a new multilayer UWB channe l to resemble the human body to detect accurate heart rate using an UWB transceiver with 3.2GHz of bandwidth in a noisy environment. We evaluate and compare the motion rate detection techniques based on variance, Fourier transform, wavelet transform, and power spectrum density, PSD. Experimental results show that our approach based on PSD is well adapted to detect the simulations by the motion phantom. The results prove an accuracy of 98% achieved by PSD method for detection of various periodic movements of the motion phantom. The result shows that this algorithm is capable of being used in real time applications and is promising for daily clinical use.
机译：超宽带（UWB）信号因其具有窄脉冲宽度的特殊优势而变得引人注目，这使其适合于对生命信号进行非侵入式遥感。在本文中，我们提出了一种基于功率谱方法的有效算法，该算法适用于UWB信号，用于无创监测和模拟心率的测量。这项研究的目的是评估在嘈杂环境中使用UWB信号无线检测人心率的实用算法。为此，首先由我们设计的运动模型模拟心率，该模型具有可变的速度和运动范围。然后，将这些数据输入到拟议的仿真框架中，该仿真框架包括一个新的类似于人体的多层UWB通道，以在嘈杂的环境中使用带宽为3.2GHz的UWB收发器来检测准确的心率。我们评估和比较基于方差，傅立叶变换，小波变换和功率谱密度PSD的运动速率检测技术。实验结果表明，我们基于PSD的方法非常适合通过运动体模检测仿真。结果证明，通过PSD方法检测运动体模的各种周期性运动，可以达到98％的精度。结果表明，该算法能够在实时应用中使用，有望用于日常临床。

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