The objective of this study was to investigate the accuracy of a wearable photoplethysmography (PPG) sensor in monitoring heart rate (HR) of sheep housed in high-temperature environments. We hypothesized that the PPG sensor would be capable of differentiating low, normal, and high HR, but would struggle to produce exact HR estimates. The sensor was open source and comprised of a microprocessor (SparkFun®ThingPlus), a photoplethysmography sensor (SparkFun® MAX30101 & MAX32664), and a data storage module (SD Card 16GB), all sewn into a nylon collar with hook-and-loop closure. Sheep (n=4) were divided into 2 groups and exposed to different thermal environments in a cross-over design. The collar was placed around the neck of the sheep during the data collection phase and the manual HR were collected twice a day using a stethoscope. Precision and accuracy of numeric heart rate estimates were analyzed in R software using Pearson correlation and root mean squared prediction errors. Random forest regression was used to classify HR based on low, medium, and high to determine opportunities to leverage the PPG sensors for HR classification. Sensitivity, specificity, and accuracy were measured to evaluate the classification approach. Our results indicated that the PPG-based sensor measured sheep HR with poor accuracy and with higher average estimates in comparison with manually measured with a stethoscope. Categorical classification of HR was also poor, with accuracies ranging from 32% to 49%. Additional work is needed focusing on data analytics, and signal optimization to further rely on PPG sensors for accurately measuring HR in sheep.
This study sought to evaluate the accuracy of a PPG (photoplethysmography) sensor designed to measure human heart rates in monitoring the distal limb pulse of healthy adult horses. We hypothesized that the PPG sensor is sensitive to placement location and orientation, and that measurement accuracies depend on placement and orientation on the limb. To evaluate this hypothesis, a completely randomized block design with a factorial treatment structure was used. Horses were considered as the block. Limb type (right front, left front, right hind, and left hind) and position of sensor (medial or lateral) were treatments, with levels arranged in a complete (4x2) factorial design. Data were collected by placing the PPG sensor on the limb of each horse (
- Award ID(s):
- 2106987
- NSF-PAR ID:
- 10469215
- Publisher / Repository:
- Frontiers
- Date Published:
- Journal Name:
- Frontiers in Animal Science
- Volume:
- 4
- ISSN:
- 2673-6225
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
Deep phenotyping of cardiac function in heart transplant patients using cardiovascular system modelsmore » « less
Key points Right heart catheterization data from clinical records of heart transplant patients are used to identify patient‐specific models of the cardiovascular system.
These patient‐specific cardiovascular models represent a snapshot of cardiovascular function at a given post‐transplant recovery time point.
This approach is used to describe cardiac function in 10 heart transplant patients, five of which had multiple right heart catheterizations allowing an assessment of cardiac function over time.
These patient‐specific models are used to predict cardiovascular function in the form of right and left ventricular pressure‐volume loops and ventricular power, an important metric in the clinical assessment of cardiac function.
Outcomes for the longitudinally tracked patients show that our approach was able to identify the one patient from the group of five that exhibited post‐transplant cardiovascular complications.
Abstract Heart transplant patients are followed with periodic right heart catheterizations (RHCs) to identify post‐transplant complications and guide treatment. Post‐transplant positive outcomes are associated with a steady reduction of right ventricular and pulmonary arterial pressures, toward normal levels of right‐side pressure (about 20 mmHg) measured by RHC. This study shows that more information about patient progression is obtained by combining standard RHC measures with mechanistic computational cardiovascular system models. The purpose of this study is twofold: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state, and to use these models to track post‐transplant recovery and outcome. To obtain reliable parameter estimates comparable within and across datasets, we use sensitivity analysis, parameter subset selection, and optimization to determine patient‐specific mechanistic parameters that can be reliably extracted from the RHC data. Patient‐specific models are identified for 10 patients from their first post‐transplant RHC, and longitudinal analysis is carried out for five patients. Results of the sensitivity analysis and subset selection show that we can reliably estimate seven non‐measurable quantities; namely, ventricular diastolic relaxation, systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and systemic arterial elastance. Changes in parameters and predicted cardiovascular function post‐transplant are used to evaluate the cardiovascular state during recovery of five patients. Of these five patients, only one showed inconsistent trends during recovery in ventricular pressure–volume relationships and power output. At the four‐year post‐transplant time point this patient exhibited biventricular failure along with graft dysfunction while the remaining four exhibited no cardiovascular complications.
-
more » « less
Abstract Objective. The objective of this study was to investigate the effects of micromagnetic stimuli strength and frequency from theMag neticPen (MagPen) on the rat right sciatic nerve. The nerve’s response was measured by recording muscle activity and movement of the right hind limb.Approach. The MagPen was custom-built to be stably held over the sciatic nerve. Rat leg muscle twitches were captured on video, and movements were extracted using image processing algorithms. EMG recordings were also used to measure muscle activity.Main results. The MagPen prototype, when driven by an alternating current, generates a time-varying magnetic field, which, according to Faraday’s law of electromagnetic induction, induces an electric field for neuromodulation. The orientation-dependent spatial contour maps of the induced electric field from the MagPen prototype have been numerically simulated. Furthermore, in thisin vivo work onµ MS, a dose-response relationship has been reported by experimentally studying how varying the amplitude (Range: 25 mV p-pthrough 6V p-p) and frequency (range: 100 Hz through 5 kHz) of the MagPen stimuli alters hind limb movement. The primary highlight of this dose-response relationship (repeated overn rats, wheren = 7) is that for aµ MS stimuli of higher frequency, significantly smaller amplitudes can trigger hind limb muscle twitch. This frequency-dependent activation can be justified by Faraday’s Law, which states that the magnitude of the induced electric field is directly proportional to the frequency.Significance. This work reports thatµ MS can successfully activate the sciatic nerve in a dose-dependent manner. The impact of this dose-response curve addresses the controversy in this research community about whether the stimulation from theseμ coils arise from a thermal effect or micromagnetic stimulation. MagPen probes do not have a direct electrochemical interface with tissue and therefore do not experience electrode degradation, biofouling, and irreversible redox reactions like traditional direct contact electrodes. Magnetic fields from theμ coils create more precise activation than electrodes because they apply more focused and localized stimulation. Finally, unique features ofµ MS, such as the orientation dependence, directionality, and spatial specificity, have been discussed. -
more » « less
Abstract To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve’s complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(
μ MS) is showing incredible promise. This neuromodulation technique uses microcoils(μ coils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday’s law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidalμ coil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) followingμ MS of the vagus. Theμ coils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000μ sec) at a frequency of 20 Hz. Under the influence of isoflurane,μ MS of the left vagus at 1000μ sec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast,μ MS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagalμ MS suggesting the drop in MAP associated with vagusμ MS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed uponμ MS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy ofμ MS on the vagal nerve. Absence of HR modulation uponμ MS could offer alternative treatment options using VNS with fewer heart-related side-effects. -
more » « less
Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fanim.2023.1103812
