论文标题
地震心动图收缩期和舒张轮廓的描述和分析
Delineation and Analysis of Seismocardiographic Systole and Diastole Profiles
论文作者
论文摘要
地震心动图（SCG）信号的基准点的精确估计是其临床用法的挑战性问题。现有文献中提出的描述技术不能同时估计SCG信号的所有临床显着点。这项研究工作的目的是提出一个描述框架，以借助PPG信号来识别IM，AO，IC，AC，PAC和MO基准点。提出的划定方法是提出了基于小波的刻板图PPG，并提出了一个包络构造方案，以估计PPG信号的突出峰。开发了一组基于幅度的决策规则，用于估计SCG舒张期，即AC，PAC和MO。随后，通过在SCG和决策规则上应用舒张期掩盖来检测收缩期，IM，AO和IC。从我们设计的数据采集界流中获得的实时SCG信号的实验结果及其分析显示了所提出的方案的有效性。此外，分析了这些估计的参数，以显示正常呼吸和呼吸困难条件之间的歧视。
Precise estimation of fiducial points of a seismocardiogram (SCG) signal is a challenging problem for its clinical usage. Delineation techniques proposed in the existing literature do not estimate all the clinically significant points of an SCG signal, simultaneously. The aim of this research work is to propose a delineation framework to identify IM, AO, IC, AC, pAC and MO fiducial points with the help of a PPG signal. The proposed delineation method processes a wavelet-based scalographic PPG and an envelope construction scheme is proposed to estimate the prominent peaks of the PPG signal. A set of amplitude histogram based decision rules is developed for estimation of SCG diastole phases, namely AC, pAC and MO. Subsequently, the systolic phases, IM, AO and IC are detected by applying diastole masking on SCG and decision rules. Experimental results on real-time SCG signals acquired from our designed data acquisition-circuitry and their analysis show the effectiveness of the proposed scheme. Additionally, these estimated parameters are analyzed to show the discrimination between normal breathing and breathlessness conditions.
