Identification of Human Breathing-States Using Cardiac-Vibrational Signal for m-Health Applications
Identification of Human Breathing-States Using Cardiac-Vibrational Signal for m-Health Applications
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DOI：
10.1109/jsen.2020.3025384
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发表时间：
2021-02-01
影响因子：
4.3
通讯作者：
Bora, Kangkana
中科院分区：
文献类型：
--
作者：
Choudhary, Tilendra;Sharma, L. N.;Bora, Kangkana
在这项工作中，提出了一种基于心震图（SCG）的呼吸状态测量方法，用于移动健康应用。所提出的框架的目的是通过识别呼吸的程度来评估人类呼吸系统，例如呼吸急促、正常呼吸以及长时间和费力的呼吸。为此，需要测量反映在SCG信号中的心脏诱发的胸壁振动。利用正交子空间投影提取同步心电信号中的SCG周期。随后，从每个SCG周期中提取15个统计上显著的形态特征。这些特征可以有效地表征由于变化的重复率而引起的生理变化。堆叠自动编码器（SAE）为基础的体系结构，用于识别不同的自动化努力水平。所提出的方法的性能进行评估，并与其他标准分类器进行比较，为1147分析SCG-节拍。所提出的方法给出了一个整体的平均准确率为91.45%，在识别三种不同的呼吸状态。性能结果的定量分析清楚地表明了所提出的框架的有效性。它可以用于各种医疗保健应用，例如预筛选医疗传感器和基于物联网的远程健康监测系统。
In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspaceprojection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory-rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG- beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.