Towards robust estimation of systolic time intervals using head-to-foot and dorso-ventral components of sternal acceleration signals

Abstract

Continuous measurement of cardiac time intervals throughout normal activities of daily living is of interest for both chronic disease management and preventive wellness monitoring. Systolic time intervals in particular - i.e., pre-ejection period (PEP) and left ventricular ejection time (LVET) - have been shown to be relevant to assessing myocardial health and performance, but are challenging to measure with wearable sensors. In this paper, we present novel methods for estimating PEP and LVET from a single three-axis accelerometer placed at the sternum, based on the measurement of cardiogenic vibrations: seismocardiography (SCG) and ballistocardiography (BCG). Although such signals have been examined in the existing literature, the analysis and interpretation has focused mainly on the dorso-ventral components only in the context of systolic time interval estimation. In this paper, we find that features extracted from the head-to-foot accelerations yield better correlations to PEP measured from impedance cardiogram (ICG) than standard approaches based on dorso-ventral components. Additionally, we examine the effects of postural variations on the correlation between PEP estimated from accelerometer and ICG signals and also on correlation between LVET estimated from both sensors. We determine that such correlations are robust to postural changes. Based on these findings, we anticipate that wearable, accelerometer based vibration measurements from standing subjects can be used for robust systolic time interval estimation in a variety of ubiquitous cardiovascular health and fitness sensing applications.

DOI: 10.1109/BSN.2015.7299377

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Cite this paper

@article{Javaid2015TowardsRE, title={Towards robust estimation of systolic time intervals using head-to-foot and dorso-ventral components of sternal acceleration signals}, author={Abdul Qadir Javaid and Nathaniel Forrest Fesmire and Mary Ann Weitnauer and Omer T. Inan}, journal={2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, year={2015}, pages={1-5} }