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Respiratory signal derived from the smartphone built-in accelerometer during a Respiratory Load Protocol

机译:在呼吸负荷协议期间从智能手机内置加速度计获得的呼吸信号

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摘要

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.
机译:我们的工作范围集中在调查智能手机内置加速度计在记录呼吸活动和推导呼吸频率方面的潜在用途。五名健康​​受试者进行了吸气负荷试验。使用智能手机的内置三轴加速度计沿x,y和z轴以及外部单轴加速度计记录右胸的偏移。同时,记录呼吸气流和吸气口压力,作为参考呼吸信号。使用基于整体经验模式分解的方案对通过智能手机在z轴上记录的胸部加速度信号进行降噪,这是一种将非平稳和非线性信号分解为固有模式函数的噪声数据辅助方法。为了将噪声振荡模式与相关模式区分开,我们使用去趋势波动分析。我们报告了整个吸气负荷协议(从0.80到0.97),智能手机的z轴加速度与参考加速度计之间存在非常强的相关性。此外,对呼吸频率的评估显示出非常强的相关性(0.98)。在从智能手机的z轴发出的胸部加速信号与呼吸气流信号之间估计的呼吸速率之间达成了良好的一致性:Bland-Altman协议极限介于-1.44和1.46每分钟呼吸之间,平均偏差为-0.01每分钟呼吸一次。这项初步研究为使用智能手机及其内置加速度计进行呼吸监测提供了宝贵的见识。

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