Cold exposure activates thermoregulatory processes through the autonomic nervous system that, while maintaining homeostasis, reduce peripheral blood flow and dexterity in the extremities. Transcutaneous median nerve stimulation (tMNS) represents a promising method for autonomic regulation through activation of parasympathetic vagal nerve afferents to the brain. However, the effect of this type of non-invasive therapy has not been investigated thoroughly in the context of thermoregulation. We analyzed peripheral skin temperature data in an ancillary study from a cohort of 19 participants who underwent two study visits of a protocol, each involving a dose-response activity, a cold pressor activity, and either tMNS or sham stimulation. Data from each protocol segment was averaged and then normalized as a percent difference from a baseline rest section. Paired t-tests and Pitman-Morgan tests were run on data from the cold pressor and dose-response activities respectively to determine statistical significance. We found that tMNS had a significant$(p=0.036)$effect of blunting peripheral skin temperature drops during cold pressor recovery as compared to sham stimulation. Additionally, tMNS had a general regulatory effect on skin temperature change during the dose-response activity, with significantly less variance than sham stimulation$(p<0.05)$. These results indicate that possible regulation of peripheral skin temperature with tMNS can serve as a therapy for cold exposure. Future work should investigate this mechanism in a larger cohort with a protocol designed to assess reactivity in temperature and dexterity to both cold and heat exposure.

Studies of stress often assume that baseline periods, stressors, and neutral conditions elicit their intended responses. This assumption may not always hold. In this study, we use a comprehensive set of cardiovascular and respiratory markers to demonstrate that factors including habituation and apprehension can lead to unintended physiological responses. Re-analyzing the data from a previous investigation of traumatic stress, we studied N = 26 participants with history of prior trauma. These participants took part in a three-hour protocol involving repeated exposure to traumatic stressors and neutral conditions. Electrocardiogram, photoplethysmogram, seismocardiogram, and respiratory effort signals were collected. Unlike previous studies, we investigated the physiological responses to each neutral condition and traumatic stressor separately, rather than aggregating over repetitions. We find that habituation reduces the physiological responses to repeated traumatic stressors. We also observe transient stress responses during the first neutral conditions of the protocol. We attribute this stress to apprehension. Notably, the stress exhibited during the first neutral condition was on par with that of the second traumatic stressor. To our knowledge, the data herein are the first to quantitatively show that apprehension during a neutral condition can produce stress responses on par with trauma recall. These results advocate against classifying periods of data as "stress" or "no stress" based solely on the protocol. Instead, studies of stress should incorporate physiological sensing to assess whether the protocol’s intended effects are consistent with observed changes in physiological markers.

Stress leads to widespread peripheral effects, including manifestations of respiratory distress. The combination of respiratory monitoring and non-invasive Peripheral Nerve Stimulation (PNS) represents a promising technological approach to quantify and reduce the physiological manifestations of stress. Such technologies require non-invasive, reliable respiratory information with adequate signal quality during various conditions. In this work, we computed an established respiratory quality index (RQI) on four respiratory signals derived from three common respiratory sensing modalities (respiratory effort (RSP), electrocardiogram-derived respiration (EDR), and Impedance Pneumography (IP)) in a complex stress protocol wherein 15 subjects underwent three stressors while receiving 1 of 3 active PNS modalities or sham. Our results indicate that the RQI of all signals changed substantially throughout the protocol with a maximal reduction from baseline of 21.28% (p< 0.001) during stressors involving speech. Further, RSP and IP resulted in the highest average quality and both were significantly higher in quality than the two EDR signals (p < 0.001). These results provide unique information that may inform the design of new technologies and studies leveraging respiratory markers for continuous stress detection and mitigation.

Existing technologies for patients with respiratory insufficiency have focused on providing reliable assistance in their breathing. However, the need for assistance in other everyday respiratory functions, such as coughing and speaking, has remained unmet in these patients. Here, we propose Exo-Abs, a wearable robotic system that can universally assist wide-ranging respiratory functions by applying compensatory force to a user's abdomen in synchronization with their air usage. Inspired by how human abdominal muscles transmit pressure to the lungs via abdominal cavity compression, a biomechanically interactive platform was developed to optimally utilize the abdominal compression while aligning the assistance with a user's spontaneous respiratory effort. In addition to the compact form factor, thorough analytic procedures are described as initial steps toward taking the human respiratory system into the scope of robotics technology. We demonstrate the validity of the overall human–system interaction with the assistance performance under three essential respiratory functions: breathing, coughing, and speaking. Our results show that the system can significantly improve the performance of all these functions by granting on-demand and self-reliant assistance to its users.

Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia to assist in triage and resuscitation. This work evaluates random forest models trained on different subsets of data from a pig model (n = 6) of absolute (bleeding) and relative (nitroglycerin-induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation. Features for the models were derived from a multi-modal set of wearable sensors, comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG) and were normalized to each subject.s baseline. The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for the best model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R2value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an AUROC of 0.80. This study: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.

The objective of this study was to investigate the measurement instrument-dependent variability in the morphology of the ballistocardiogram (BCG) waveform in human subjects and computational methods to mitigate the variability. The BCG was measured in 22 young healthy subjects using a high-performance force plate and a customized commercial weighing scale under upright standing posture. The timing and amplitude features associated with the major I, J, K waves in the BCG waveforms were extracted and quantitatively analyzed. The results indicated that 1) the I, J, K waves associated with the weighing scale BCG exhibited delay in the timings within the cardiac cycle relative to the ECG R wave as well as attenuation in the absolute amplitudes than the respective force plate counterparts, whereas 2) the time intervals between the I, J, K waves were comparable. Then, two alternative computational methods were conceived in an attempt to mitigate the discrepancy between force plate versus weighing-scale BCG: a transfer function and an amplitude-phase correction. The results suggested that both methods effectively mitigated the discrepancy in the timings and amplitudes associated with the I, J, K waves between the force plate and weighing-scale BCG. Hence, signal processing may serve as a viable solution to the mitigation of the instrument-induced morphological variability in the BCG, thereby facilitating the standardized analysis and interpretation of the timing and amplitude features in the BCG across wide-ranging measurement platforms.

We investigated the relationship between carotid artery blood pressure (BP) and distal pulse volume waveforms (PVRs) via subject-specific mathematical modeling. We conceived three physical models to define the relationship: a tube-load model augmented with a gain (TLG), Voigt (TLV), and standard linear solid (TLS) models. We compared these models using PVRs measured via BP cuffs at an upper arm and an ankle as well as carotid artery tonometry waveform collected from 133 subjects. At both upper arm and ankle, PVR was related to carotid artery tonometry by TLV and TLS models better than by TLG model; when root-mean-squared over all the subjects, the systolic and diastolic BP errors between measured carotid artery tonometry waveform and the one estimated from distal PVR reduced from 4.3 mmHg and 4.6 mmHg (TLG) to 1.1 mmHg and 1.0 mmHg (TLS) for the upper arm (p < 0.0167), and from 2.1 mmHg and 1.7 mmHg (TLG) to 2.1 mmHg and 1.5 mmHg (TLV) for the ankle. Further, TLV and TLS models exhibited superior Akaike's Information Criterion (AIC) in both locations than TLG model. However, the difference between TLG versus TLV and TLS models associated with the ankle was not large. Therefore, the relationship of central arterial BP to arm PVR arises from both wave reflection and viscoelasticity while the relationship to ankle PVR mainly arises from wave reflection. These findings may imply that an effective subject-specific transfer function for estimating accurate central arterial BP from an arm PVR should account for the impact of viscoelasticity.

In this study, we present a system identification approach to the mathematical modeling of hemodynamic responses to vasopressor-inotrope agents. We developed a hybrid model called the latency-dose-response-cardiovascular (LDC) model that incorporated 1) a low-order lumped latency model to reproduce the delay associated with the transport of vasopressor-inotrope agent and the onset of physiological effect, 2) phenomenological dose-response models to dictate the steady-state inotropic, chronotropic, and vasoactive responses as a function of vasopressor-inotrope dose, and 3) a physiological cardiovascular model to translate the agent's actions into the ultimate response of blood pressure. We assessed the validity of the LDC model to fit vasopressor-inotrope dose-response data using data collected from five piglet subjects during variable epinephrine infusion rates. The results suggested that the LDC model was viable in modeling the subjects' dynamic responses: After tuning the model to each subject, the r (2) values for measured versus model-predicted mean arterial pressure were consistently higher than 0.73. The results also suggested that intersubject variability in the dose-response models, rather than the latency models, had significantly more impact on the model's predictive capability: Fixing the latency model to population-averaged parameter values resulted in r(2) values higher than 0.57 between measured versus model-predicted mean arterial pressure, while fixing the dose-response model to population-averaged parameter values yielded nonphysiological predictions of mean arterial pressure. We conclude that the dose-response relationship must be individualized, whereas a population-averaged latency-model may be acceptable with minimal loss of model fidelity.

This paper presents a novel minimally invasive method for quantifying blood pressure (BP) wave reflection in the arterial tree. In this method, two peripheral BP waveforms are analyzed to obtain an estimate of central aortic BP waveform, which is used together with a peripheral BP waveform to compute forward and backward pressure waves. These forward and backward waves are then used to quantify the strength of wave reflection in the arterial tree. Two unique strengths of the proposed method are that 1) it replaces highly invasive central aortic BP and flow waveforms required in many existing methods by less invasive peripheral BP waveforms, and 2) it does not require estimation of characteristic impedance. The feasibility of the proposed method was examined in an experimental swine subject under a wide range of physiologic states and in 13 cardiac surgery patients. In the swine subject, the method was comparable to the reference method based on central aortic BP and flow. In cardiac surgery patients, the method was able to estimate forward and backward pressure waves in the absence of any central aortic waveforms: on the average, the root-mean-squared error between actual versus computed forward and backward pressure waves was less than 5 mmHg, and the error between actual versus computed reflection index was less than 0.03.

This paper presents a comparative study on the relative performance of alternative strategies for estimating an individualized central aortic blood pressure (BP) waveform. Based on the transmission line representation of the central aortic-radial arterial line, a fully individualized (ITF 1), two partially individualized (ITF 2 and ITF 3 ), and a fully nonindividualized (NITF) transfer functions (i.e., frequency-dependent central aortic-radial BP relationships) were constructed using experimental data collected from nine swine subjects. The central aortic BP waveforms estimated by these transfer functions were compared against their measured gold standards, with the root-mean-squared waveform error and the absolute errors associated with systolic and pulse pressures as performance measures. Overall, the advantage of the individualized over the nonindividualized approach was modest but significant. The superiority of the individualized approach to its nonindividualized counterpart was increasingly pronounced under nonnominal or extreme physiologic conditions, as the subject's pulse transit time deviated from the averaged nominal value. The results suggest that the use of a fully individualized transfer function (ITF 1) is strongly recommended for nonnominal physiologic conditions, whereas a partially (ITF 2 and ITF 3) or even fully nonindividualized transfer function (NITF) may also yield acceptable performance under nominal physiologic conditions.

This paper presents a systematic approach to system identification and closed-loop control of end-tidal carbon dioxide partial pressure (PETCO2) in mechanically ventilated patients. An empirical model consisting of a linear dynamic system followed by an affine transform is proposed to derive a low-order and high-fidelity representation that can reproduce the positive and inversely proportional dynamic input-output relationship between PETCO2 and minute ventilation (MV) in mechanically ventilated patients. The predictive capability of the empirical model was evaluated using experimental respiratory data collected from eighteen mechanically ventilated human subjects. The model predicted PETCO2 response accurately with a root-mean-squared error (RMSE) of 0.22+/-0.16 mmHg and a coefficient of determination (r2) of 0.81+/-0.18 (mean+/-SD) when a second-order rational transfer function was used as its linear dynamic component. Using the proposed model, a closedloop control method for PETCO2 based on a proportionalintegral (PI) compensator was proposed by systematic analysis of the system root locus. For the eighteen mechanically ventilated patient models identified, the PI compensator exhibited acceptable closed-loop response with a settling time of 1.27+/- 0.20 min and a negligible overshoot (0.51+/-1.17%), in addition to zero steady-state PETCO2 set point tracking. The physiologic implication of the proposed empirical model was analyzed by comparing it with the traditional multi-compartmental model widely used in pharmacological modeling.

This paper presents a new approach to the estimation of unknown central aortic blood pressure waveform from a directly measured peripheral blood pressure waveform, in which a physics-based model is employed to solve for a subject- and state-specific individualized transfer function (ITF). The ITF provides the means to estimate the unknown central aortic blood pressure from the peripheral blood pressure. Initial proof-of-principle for the ITF is demonstrated experimentally through an in vivo protocol. In swine subjects taken through wide range of physiologic conditions, the ITF was on average able to provide central aortic blood pressure waveforms more accurately than a nonindividualized transfer function. Its usefulness was most evident when the subject's pulse transit time deviated from normative values. In these circumstances, the ITF yielded statistically significant reductions over a nonindividualized transfer function in the following three parameters: 1) 30% reduction in the root-mean-squared error between estimated versus actual central aortic blood pressure waveform (p < 10 (-4)), 2) >50% reduction in the error between estimated versus actual systolic and pulse pressures ( p < 10 (-4)), and 3) a reduction in the overall breakdown rate (i.e., the frequency of estimation errors >3 mmHg, p < 10 (-4)). In conclusion, the ITF may offer an attractive alternative to existing methods that estimates the central aortic blood pressure waveform, and may be particularly useful in nonnormative physiologic conditions.