Heart failure (HF) is one of the most major challenges faced by society today, claiming hundreds of thousands of American lives each year and costing more than 30 billion Medicare dollars annually. The ultimate goal of this research is to create an unobtrusive wearable system for continuously monitoring HF patients in naturalistic settings, automatically assessing their risk of experiencing an exacerbation, and providing feedback to caregivers and the patients themselves. The central innovation that will support these efforts is the proposed measurement of hemodynamic responses to stressors experienced in normal activities of daily living (e.g., walking, climbing stairs). The measurement of such hemodynamic responses will be enabled by wearable ballistocardiography (BCG). The following four specific aims are proposed for the research: (1) to elucidate the underlying mechanisms involved in the genesis of wearable ballistocardiogram (BCG) signals; (2) to develop novel predictive analytics algorithms for BCG signals measured from HF patients at home; (3) to design and implement a wearable sensing system for estimating cardiac output (CO), blood pressure (BP), and indirect calorimetry from ambulant subjects; and (4) to evaluate the wearable sensing system with healthy and HF patients during cardiopulmonary stress testing, and to pilot the new system at home for a small population of patients.
The first aim will build a strong foundation for better understanding the wearable BCG signal - a measurement of body vibrations in response to the heartbeat - and will inform the placement and modality of the sensor for optimizing the sensing. Furthermore, the evaluation of this wearable prototype will include usability testing to assess comfort and robustness to practical challenges (e.g., motion artifacts, the device rubbing on clothing), and based on the results the design will be refined and improved. While we anticipate that the wearable will provide the best solution, the project risk is mitigated through the more mature, existing scale- based system. Successful completion of this project could ultimately reduce HF related hospitalizations, and thus both improve quality of life for elderly Americans, and reduce overall healthcare costs.
Heart failure (HF) affects five million Americans and costs $30 billion dollars annually. The mainstay of HF treatment is monitoring of symptoms-a 'reactive' approach that is often too late to prevent a more serious outcome. We propose to design and implement new technologies for monitoring HF at home, and ultimately predicting worsening of symptoms before they occur: a 'proactive' approach that will ultimately reduce both the incidence and cost of HF.
| Inan, Omer T; Baran Pouyan, Maziyar; Javaid, Abdul Q et al. (2018) Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients. Circ Heart Fail 11:e004313 |
| Ashouri, Hazar; Hersek, Sinan; Inan, Omer T (2018) Universal Pre-Ejection Period Estimation Using Seismocardiography: Quantifying the Effects of Sensor Placement and Regression Algorithms. IEEE Sens J 18:1665-1674 |
| Ashouri, Hazar; Inan, Omer T (2017) Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings. IEEE Sens J 17:3805-3813 |
| Javaid, Abdul Q; Ashouri, Hazar; Dorier, Alexis et al. (2017) Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health. IEEE Trans Biomed Eng 64:1277-1286 |
