A Fast Respiratory Rate Estimation Method using Joint Sparse Signal Reconstruction based on Regularized Sparsity Adaptive Matching Pursuit.

Zhongyi Han, Qun Wang, Liang Yue, Zhiwen Liu
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Abstract

Many algorithms have been used to estimate respiratory rate (RR) from Photoplethysmography (PPG) recently. However, the accuracy and time consumption are still a challenging issue. In this paper, we propose a novel algorithm for RR estimation using Joint Sparse Signal Reconstruction (JSSR) based on Regularized Sparsity Adaptive Matching Pursuit (RSAMP) in a real-time fashion. The algorithm has been tested on Capnobase dataset and the results showed that the mean absolute error (MAE) and root mean squared error between estimates and references are 1.09 breaths per minute (bpm) and 2.44 bpm, respectively. And our method only costs 0.54 seconds for calculation.
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一种基于正则稀疏度自适应匹配追踪的联合稀疏信号重构呼吸频率快速估计方法。
近年来,许多算法被用于从光容积脉搏波(PPG)中估计呼吸速率(RR)。然而,准确性和时间消耗仍然是一个具有挑战性的问题。本文提出了一种基于正则化稀疏度自适应匹配追踪(RSAMP)的联合稀疏信号重构(JSSR)实时RR估计算法。在Capnobase数据集上对该算法进行了测试,结果表明,估计值与参考值之间的平均绝对误差(MAE)和均方根误差分别为1.09次/分钟和2.44次/分钟。我们的方法只需要0.54秒的计算时间。
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