Ashouri Hazar, Inan Omer T
School of Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, GA 30332 USA.
IEEE Sens J. 2017 Jun 15;17(12):3805-3813. doi: 10.1109/JSEN.2017.2701349. Epub 2017 May 4.
Seismocardiography (SCG), the measurement of the local chest vibrations due to the movements of blood and the heart, is a non-invasive technique for assessing myocardial contractility via the pre-ejection period (PEP). Recently, SCG-based extraction of PEP has been shown to be an effective means of classifying decompensated from compensated heart failure patients, and thus can be potentially used for monitoring such patients at home. Accurate extraction of PEP from SCG signals hinges on lab-based population data (i.e., regression curves) linking particular time-domain features of the SCG signal to corresponding features from reference standard bulky instruments such as impedance cardiography (ICG). Such regression curves, in the case of SCG, have always been estimated based on the "ideal" positioning of the SCG sensor on the chest. However, in settings such as the home where users may position the SCG measurement hardware on the chest without supervision, it is likely that the sensor will not always be placed exactly on this "ideal" location on the sternum, but rather on other positions on the chest as well. In this study, we show for the first time that the regression curve for estimating PEP from SCG signals differs significantly as the position of the sensor changes. We further devise a method to automatically detect when the sensor is placed in any position other than the desired one in order to avoid inaccurate systolic time interval estimation. Our classification algorithm for this purpose resulted in 0.83 precision and 0.82 recall when classifying whether the sensor is placed in the desired position or not. The classifier was tested with heartbeats taken both at rest, and also during exercise recovery to ensure that waveform changes due to positioning could be accurately discriminated from those due to physiological effects.
地震心音图描记法（SCG）是一种通过测量血液和心脏运动引起的胸部局部振动来评估心肌收缩力的非侵入性技术，它通过射血前期（PEP）来实现这一目的。最近的研究表明，基于SCG提取PEP是区分失代偿性心力衰竭患者和代偿性心力衰竭患者的有效方法，因此有可能用于在家中对这类患者进行监测。从SCG信号中准确提取PEP取决于基于实验室的群体数据（即回归曲线），这些数据将SCG信号的特定时域特征与参考标准大型仪器（如阻抗心动图（ICG））的相应特征联系起来。就SCG而言，此类回归曲线一直是基于SCG传感器在胸部的“理想”位置进行估计的。然而，在诸如家庭等用户可能在无人监督的情况下将SCG测量硬件放置在胸部的环境中，传感器很可能不会总是精确地放置在胸骨上的这个“理想”位置，而是也会放置在胸部的其他位置。在本研究中，我们首次表明，随着传感器位置的变化，从SCG信号估计PEP的回归曲线会有显著差异。我们进一步设计了一种方法，用于自动检测传感器何时放置在期望位置以外的任何位置，以避免收缩期时间间隔估计不准确。为此目的，我们的分类算法在对传感器是否放置在期望位置进行分类时，精确率为0.83，召回率为0.82。该分类器在静息和运动恢复期间采集的心跳数据上进行了测试，以确保能够准确区分由于位置变化引起的波形变化和由于生理效应引起的波形变化。
