By Omer T. Inan
By 2030, 40% of Americans will suffer from cardiovascular disease and the annual costs will approach $1 trillion. Cardiovascular monitoring at home could improve the quality of care and life for these millions of patients, and reduce healthcare costs for all Americans. This article describes technologies developed for unobtrusively assessing the mechanical aspects of cardiovascular function at home using ballistocardiography (BCG). A weighing scale was modified for BCG measurement, and tested in collaboration with engineers and clinicians to demonstrate clinical relevance. Recent work focusing on creating a framework for measuring the BCG using wearable sensors is also discussed.
By 2030, the American Heart Association projects that 40% of Americans (150 million) will suffer from cardiovascular disease and the annual costs will approach $1 trillion . Cardiovascular monitoring at home could improve the quality of care and life for these millions of patients, and reduce healthcare costs for all Americans. Rather than reacting to catastrophic cardiac events such as heart attacks or strokes in the emergency room, care could be delivered proactively by tailoring treatment strategies to the changing needs of the patients. To accelerate this transition from reactive to proactive care, we need systems-level innovations in multi-modal physiological monitoring and signal interpretation. These solutions would leverage advances in embedded systems and sensor technology to achieve accurate and robust monitoring of clinically relevant parameters in the home.
In work started at Stanford during my doctoral studies advised by Professor Gregory Kovacs, and now continuing at the Georgia Institute of Technology in my group (Inan Lab), my research has focused on creating such systems for non-invasive cardiovascular monitoring at home: specifically, I am interested in improving the available methods for unobtrusively measuring mechanical aspects of cardiovascular function. One example is a modified electronic weighing scale designed to monitor the electrical and mechanical health of the heart [2-4]. With this scale, I measured fluctuations in bodyweight resulting from the movement of blood throughout the vasculature – the ballistocardiogram (BCG) – and combined physiologic insights with feature extraction concepts to interpret the BCG signals and uncover their potential clinical relevance. The BCG is a measure of the mechanical forces of the body in reaction to cardiac ejection of blood into the vasculature, and was originally measured using elaborate tables and beds in the mid-1900s [e.g., ]. By connecting the strain gauges in an electronic weighing scale to custom electronics, and developing dedicated algorithms for pre-processing and feature extraction, I was able to measure and interpret the BCG signal measured from a person standing upright on a scale .
Subsequently, I conducted multiple clinical studies with healthy and diseased populations in collaboration with cardiologists in the Stanford Medical School, local clinics, and recently with Dr. Liviu Klein and Professor Shuvo Roy at the University of California, San Francisco School of Medicine to demonstrate the efficacy of this device for home health monitoring. In the work at Stanford, together with other engineers (Drs. Giovangrandi, Etemadi, and Wiard) and clinicians (Drs. Liang and Froelicher), I demonstrated that the BCG signals from the modified weighing scale could accurately track changes in a person’s cardiac output , cardiac contractility , and beat-by-beat left ventricular function during arrhythmias . On the engineering side, we found that the BCG measurements were repeatable, and developed techniques for improving the robustness to motion artifacts and floor vibrations [7-8].
Most recently, I am leading efforts at Georgia Tech in developing systems for accurately measuring the BCG signal with wearable sensors. This could allow the assessment of a person’s cardiovascular performance during normal activities of daily living, by monitoring the hemodynamics in response to various stressors. Previous efforts to measure the BCG with wearable sensors have used accelerometers affixed to the torso skin or head, but the resultant waveforms from these measurements are significantly different from those measured in the traditional BCG literature and from the scale system. Their interpretation as “BCG” signals is thus unclear, and techniques developed for BCG signals may not directly transfer over to the wearable domain. Fundamentally, the BCG represents the movements of the center of mass of the body in response to the heartbeat, while an accelerometer on the body measures the local accelerations of the torso skin or head. My group has recently found that relating these locally measured accelerations to whole body displacements is possible with a transfer function, but can be subject-dependent, and we have thus made recommendations for optimal placement locations of the accelerometer for capturing BCG information . These findings can help pave the way for future work in developing technologies for continuously assessing hemodynamics at home, and thus enabling proactive care.
This work was carried out in collaboration with Drs. Gregory T. A. Kovacs, Laurent Giovangrandi, Mozziyar Etemadi, and Richard Wiard at Stanford, and Drs. Liviu Klein and Shuvo Roy at the University of California at San Francisco.
For Further Reading
1. P. A. Heidenreich, J. G. Trogdon, O. A. Khavjou, J. Butler, K. Dracup, et al. (2011) Forecasting the Future of Cardiovascular Disease in the United States: A Policy Statement From the American Heart Association. Circulation 123 (8):933-944.
3. O. T. Inan, M. Etemadi, A. Paloma, L. Giovangrandi, G. T. A. Kovacs Non-invasive cardiac output trending during exercise recovery on a bathroom-scale-based ballistocardiograph. Physiol Meas 30 (3):261.
5. Starr I, A. J. Rawson, H. A. Schroeder HA, et al. (1939) Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart’s recoil and the blood’s impacts; the ballistocardiogram. American Journal of Physiology 127: 1-28.
7. O. T. Inan, G. T. A. Kovacs, L. Giovangrandi, (2010) Evaluating the Lower-Body Electromyogram Signal Acquired From the Feet As a Noise Reference for Standing Ballistocardiogram Measurements. IEEE T-ITB 14 (5):1188-1196.
8. O. T. Inan, M. Etemadi, Widrow B, Kovacs GTA (2010) Adaptive Cancellation of Floor Vibrations in Standing Ballistocardiogram Measurements Using a Seismic Sensor as a Noise Reference. IEEE TBME 57 (3):722-727.
9. Wiens A, M. Etemadi, L. Klein, S. Roy, O. T. Inan, (In Review) Wearable Ballistocardiography: Preliminary Methods for Mapping Surface Vibration Measurements to Whole Body Forces. Invited Paper to be presented at the IEEE Engineering in Medicine and Biology Conference, Chicago, IL.
Omer T. Inan is an Assistant Professor of Electrical and Computer Engineering at the Georgia Institute of Technology, where he researches physiological and biomedical sensing and monitoring. He received his PhD in Electrical Engineering from Stanford University. Read more