基于心震图信号的移动健康平台呼吸状态检测器设计
Design of Breathing-states Detector for m-Health Platform using Seismocardiographic Signal
AI总结：
本文提出一种基于心震图信号的移动健康呼吸状态检测方法，利用正交子空间投影提取SCG周期并结合堆叠自编码器识别呼吸困难、正常及费力呼吸，在1147个心搏上达到91.45%平均准确率。
摘要：
本文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（m-health）应用。所提框架的目标是通过识别呼吸程度（如呼吸困难、正常呼吸以及长而费力的呼吸）来评估人体呼吸系统。为此，需要测量由心脏引起的胸壁振动，这些振动反映在SCG信号中。利用正交子空间投影，并借助同步的心电图（ECG）信号来提取SCG周期。随后，从每个SCG周期中提取十五个具有统计显著性的形态学特征。这些特征能够有效表征因呼吸频率变化而引起的生理变化。采用基于堆叠自编码器（SAE）的架构来识别不同的呼吸努力水平。在1147个分析的SCG心搏上，对所提方法的性能进行了评估，并与其他标准分类器进行了比较。所提方法在识别三种不同呼吸状态时，总体平均准确率达到91.45%。性能结果的定量分析清楚地表明了所提框架的有效性。该方法可用于各种医疗保健应用，例如预筛查医疗传感器和基于物联网的远程健康监测系统。
英文摘要：
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 subspace projection 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
