Alterations of pulmonary vascular afterload in exercise-induced pre- and post-capillary pulmonary hypertension.

Exercise imposes increased pulmonary vascular afterload based on rises in pulmonary artery (PA) wedge pressure, declines in PA compliance, and resistance-compliance time. In health, afterload stress stabilizes during steady-state exercise. Our objective was to examine alterations of these exercise-associated stresses in states of pre- and post-capillary pulmonary hypertension (PH). PA hemodynamics were evaluated at rest, 2 and 7 min of steady-state exercise at moderate intensity in patients who exhibited Pre-capillary (n = 22) and post-capillary PH (n = 22). Patients with normal exercise hemodynamics (NOR-HD) (n = 32) were also studied. During exercise in all groups, PA wedge pressure increased at 2 min, with no further change at 7 min. In post-capillary PH and NOR-HD, increases in PA diastolic pressure and diastolic pressure gradient remained stable at 2 and 7 min of exercise, while in pre-capillary PH, both continued to increase at 7 min. The behavior of the diastolic pressure gradient was linearly related to the duration of resistance-compliance time at rest (r2  = 0.843) and exercise (r2  = 0.760). Exercise resistance-compliance time was longer in pre-capillary PH associated with larger increases in diastolic pressure gradient. Conversely, resistance-compliance time was shortest in post-capillary PH compared to pre-capillary PH and NOR-HD and associated with limited increases in exercise diastolic pressure gradient. During steady-state, modest-intensity exercise-specific patterns of pulmonary vascular afterload responses were observed in pre- and post-capillary PH relative to NOR-HD. Longer resistance-compliance time related to greater increases in PA diastolic pressure and diastolic pressure gradients in pre-capillary PH, while shorter resistance-compliance time appeared to limit these increases in post-capillary PH.

Load Distribution Analysis for Weight and Ballistocardiogram Measurements of Heart Failure Patients using a Bed Scale.

Ballistocardiogram (BCG) is an emerging tool with the potential to monitor heart failure (HF) patients. A close association of the weight to the BCG as an intermediate signal source requires a careful design, where events such as saturation of the weight signal can result in the loss of the BCG. This work closely examined the factors around the weight while load cells placed under each support of a bed collected the BCG (e.g., body weight, distribution over the four supports of the bed). Following the calibration of weights based on the location of the polls, the study examined the ratios of loads in head-foot and lateral directions. The head-foot ratio was also correlated to the height. Twelve non-obese HF patients were recruited, and the weight and BCG were appropriately measured, where the average error of the weight measurements was 0.45 ± 0.30%. The mean ratio of the loads between head to foot sensors was 3.2 ± 0.7 with a maximum ratio of 4.5, showing that the head-ward sensors supported greater body weight. The ratio of the loads between the right to left sensors was 1.2 ± 0.1. The height and the head-to-foot ratio had an inverse correlation (r = 0.52). Based on the analysis, the head-ward sensors should have a higher capacity of up to three times that of the foot-ward sensors to prevent any signal saturation. Mobility issues were observed in some subjects, attributing to the lateral imbalance. These novel findings based on the end-users (i.e., HF population) may allow better allocation of conditioning resources to obtain the BCG (e.g., optimally adjusted sensitivity).

Ballistocardiogram Based Identity Recognition: Towards Zero-Effort Health Monitoring in an Internet-of-Things (IoT) Environment.

Ballistocardiography (BCG), a measure of body vibrations due to ejection of blood into aorta, has the potential to become a 'zero-effort' cardiovascular health monitoring technology, i.e., a technology that requires little or no engagement on part of the user for its operation. In order for any zero-effort monitoring technology to function without any input from the user, it is important that such a methodology can accurately perform identity recognition and thus continuously provide results and feedback to each user. However, most of the recent work on BCG has focused mainly on the estimation of parameters related to mechanical health and the use of BCG to identify a user has not been explored thoroughly. In this paper, we examine, using discrete cosine transform based features and multi-class linear classifier, the use of BCG heartbeats for identity recognition. We demonstrate from the BCG data of 52 healthy subjects collected using a modified floor tile that an average accuracy of 96.15% can be achieved for correct identification of each subject standing on the tile. Based on these results, we anticipate that such a BCG system, trained for a set of users, can be easily installed at different locations in the house and provide continuous and unobtrusive feedback to users for diagnostic monitoring and quantified-self.

Unobtrusive Detection of Simulated Orthostatic Hypotension and Supine Hypertension Using Ballistocardiogram and Electrocardiogram of Healthy Adults.

Effective management of neurogenic orthostatic hypotension and supine hypertension (SH-OH) due autonomic failure requires a frequent and timely adjustment of medication throughout the day to maintain the blood pressure (BP) within the normal range, i.e., an accurate depiction of BP is a key prerequisite of effective management. One of the emerging technologies that provide one's circadian and long-term physiological status with increased usability is unobtrusive zero-effort monitoring. In this paper, a zero-effort device, a floor tile, was used to develop an unobtrusive BP monitoring technique. Namely, RJ-interval, the time between the J-peak of a ballistocardiogram and the R-peak of an electrocardiogram, was used to develop a classifier that can detect changes in systolic BP (SBP) induced by the Valsalva maneuver on healthy adults (i.e., a simulated SH-OH). A t-test was used to show statistical differences between the mean RJ-intervals of decreased SBP, baseline, and increased SBP. Following the t-test, a classifier that detected a change in SBP was developed based on a naïve Bayes classifier (NBC). The t-test showed a clear statistical difference between the mean RJ-intervals of the increased SBP, baseline, and decreased SBP. The NBC-based classifier was able to detect increased SBP with 89.3% true positive rate (TPR), 100% true negative rate (TNR), and 94% accuracy and detect decreased SBP with 92.3% TPR, 100% TNR, and 95% accuracy. The analysis showed strong potential in using the developed classifier to assist monitoring of people with SH-OH; the algorithm may be used clinically to detect a long-term trend of symptoms of SH-OH.