IntroductionHypervolemia (volume overload) and hypovolemia (volume deficit) are sub-optimal blood volume conditions that influence overall circulation and organ function, with substantial shifts posing serious risk of death. In both conditions, the body’s response mechanisms can compensate for sub-optimal volume up to a point at which the cardiovascular system decompensates. Most current methods for assessing blood volume status are either limited to clinical settings or are inaccurate, posing a particular challenge for triaging or monitoring in austere environments. Recent work has aimed at developing novel wearable sensing and machine learning technologies to yield early and accurate signs of sub-optimal volume status prior to decompensation. Seismocardiogram (SCG) signals, representing vibrations of the chest wall due to heartbeat, have been leveraged as a non-invasive and convenient means of extracting cardiac timing representative of sub-optimal volume status. In this work, we aimed to elucidate the changes in morphological variability in the SCG signal in both hypervolemic and hypovolemic conditions. We hypothesized that the variability in the SCG signal would be heightened in decompensated versus compensated states, since the decompensated state would represent hemodynamic conditions that were less stable and thus more variable on a beat-by-beat basis. MethodsTwo datasets were fused for this analysis: 11 heart failure patients with acute decompensation and hypervolemia undergoing vasodilation (thus transitioning to a compensated state), and 15 swine undergoing a volume depletion experiment (hypovolemia). SCG signal consistency, representing the inverse of signal variability, was computed using 60 seconds of data extracted each from the compensation and decompensation periods. ResultsWhile there was no significant change in signal consistency from compensation to decompensation in the dorso-ventral nor head-to-foot SCG axes for either the hypervolemia or hypovolemia populations, we observed a significant change in signal consistency from compensation to decompensation in the lateral axis for both the hypervolemia (p = 0.042) and hypovolemia (p = 0.012) populations. DiscussionThese results demonstrate an important relationship between compensation status and SCG morphology variability, particularly in the underutilized SCG lateral axis. This paper thus sets a foundation for enabling future algorithms leveraging SCG variability to provide baseline-free estimation capability of sub-optimal volume status in field settings.

Background Acute stress, in moderation, helps to prepare the body to overcome mental and physical challenges. However, excessive bouts of acute stress can be detrimental to the cardiovascular system and are a risk factor for cardiovascular disease and sudden cardiac death. Transcutaneous median nerve stimulation (tMNS) is a promising therapy for the mitigation of acute stress through peripheral neuromodulation, but the optimal delivery of tMNS for stress mitigation may require continuous monitoring of acute stress events for targeted delivery. Objective The purpose of this study was to develop a wearable system capable of continuous closed-loop acute stress monitoring and mitigation through non-invasive cardiovascular sensing and tMNS respectively. Methods A wearable wrist-worn device capable of sensing three channels of photoplethysmogram (PPG) and tri-axial accelerometry was designed. Pulse rate (PR) and PPG amplitude (PPGamp) were extracted from the acquired green PPG signal, while tMNS was delivered at varying intensities by custom-designed analog circuitry onboard the device. A companion app was used to wirelessly set stimulation levels by communicating with the device's microcontroller using Bluetooth low energy. The device was validated against bench-top sensors in a study with 19 healthy participants involving acute mental and physical stressors as well as tMNS. Repeated-measures correlation and Bland-Altman analyses were performed to compare PR extracted from 9904 5-s windows of PPG from the device and heart rate (HR) extracted beat-by-beat from bench-top electrocardiogram (ECG) and averaged across the same windows. Statistical tests were also performed to analyze differences in mean PR and PPGamp from baseline metrics across the acute stress and tMNS protocol. Results PR extracted from our device correlated (r = 0.871, p < 0.001) and agreed (mean difference: 0.51 bpm, 95 % limits of agreement: 6.58 bpm, 5.57 bpm) strongly with HR extracted from bench-top ECG. We found decreases in mean PPGamp from baseline during stressors, while application of tMNS alongside stressors increased PPGamp back to baseline levels, and continued delivery of tMNS post-stressor further increased PPGamp to a significant difference from baseline. Significant reductions in PR as compared to baseline post-physical stressor also mirrored these findings, suggesting that our wearable device can track elevations in acute stress through cardiovascular monitoring while also mitigating the effects of acute stress through tMNS. Conclusion Our device is the first wearable, to our knowledge, to enable continuous monitoring of acute stress through cardiovascular sensing and feature extraction while mitigating acute stress through peripheral neuromodulation. Future work should test the device in ambulatory settings and investigate potential applications for clinical use-cases such as anxiety or trauma disorders.

Forecasting the near-exact moments of cardiac phases is crucial for several cardiovascular health applications. For instance, forecasts can enable the timing of specific stimuli (e.g., image or text presentation in psycholinguistic experiments) to coincide with cardiac phases like systole (cardiac ejection) and diastole (cardiac filling). This capability could be leveraged to enhance the amplitude of a subject's response, prompt them in fight-or-flight scenarios or conduct retrospective analysis for physiological predictive models. While autoregressive models have been employed for physiological signal forecasting, no prior study has explored their application to forecasting aortic opening and closing timings. This work addresses this gap by presenting a comprehensive comparative analysis of autoregressive models, including various forms of Kalman filter-based implementations, that use previously detected R-peak, aortic opening, and closing timings from electrocardiogram (ECG) and seismocardiogram (SCG) to forecast subsequent timings. We evaluate the robustness of these models to noise introduced in both SCG signals and the output of feature detectors. Our findings indicate that time-varying and multi-feature algorithms outperform others, with forecast errors below 2 ms for R-peak, below 3 ms for aortic opening timing, and below 10 ms for aortic closing timing. Importantly, we elucidate the distinct advantages of integrating multi-feature models, which improve noise robustness, and time-varying approaches, which adapt to rapid physiological changes. These models can be extended to a wide range of short-term physiological predictive systems, such as acute stress detection, neuromodulation sensor feedback, or muscle fatigue monitoring, broadening their applicability beyond cardiac feature forecasting.

Non-invasive, direct Achilles tendon (AT) load measurements have been a long-standing objective in biomechanics toward elucidating underlying muscle-tendon dynamics during normal and pathological human locomotion. However, traditional methods fail to capture subcutaneous changes due to the disconnect between externally measured forces and internal tendon dynamics. In this study, we introduce Active Acoustics (AA) as a non-invasive approach for measuring AT loads, building on recent developments by leveraging continuous mechanical stimulation in place of intermittent taps or bursts. We assessed AA’s performance against Inverse Dynamics (ID) in 10 healthy subjects. We collected data from 13 tasks designed to capture a wide range of AT force, displacement, and velocity conditions. AA successfully tracked dynamic changes in AT loading while maintaining low computational complexity, achieving the shortest filtering latency in synthetic benchmark tests compared to prior methods. Our benchmark evaluation demonstrated a strong Pearson correlation (r=0.95 ±0.02, n=6) during active, isometric contractions, supporting the feasibility of continuous stimulation for AT load measurements. Across the 13 tasks, AA captured task-specific variations in AT loading, revealing nuanced effectiveness dependent on the specific ankle joint dynamic conditions, and the need for improved under-the-skin reference signals. The continuous stimulation approach enabled a higher output frequency (500 Hz vs 50/5 Hz previous work) and demonstrated consistent performance at an experimentally optimized stimulation frequency of 750 Hz. This study underscores AA’s potential for real-time, low-latency applications in assistive and rehabilitative devices. Future research should explore sensor optimization, expanded task sets, and application-specific features to further enhance AA's utility.

Seismocardiography (SCG), a non-invasive method for capturing cardio-mechanical signals, is often susceptible to noise and motion artifacts. Current approaches primarily use automated algorithms and machine learning techniques for signal quality indexing and fiducial point detection. However, validation is hindered by the scarcity of standardized, high-quality annotated datasets. To facilitate the manual annotation process of SCG signals, we developed an open-source graphical user interface, providing a simple, efficient, and accurate tool. Using data from a porcine hypovolemia protocol, we annotated SCG waveforms for signal quality scores and annotated cardiac timing intervals (e.g., aortic opening and aortic closing) with 17,059 SCG heart beats in total across six porcine subjects. Validating the rigor and consistency of annotations, performed analyses for inter-annotator agreements resulted high agreements achieving strong correlations against gold-standard catheter-based measurements for aortic opening (AO) (r = 0.926) and aortic closing (AC) (r = 0.911). This expertly annotated dataset supports advancements in real-time cardiac monitoring, denoising, and diagnostic applications, and enhances reproducibility and comparability in SCG research across the biomedical signal processing community.

Objective: Wearable sensing for capturing knee acoustic emissions (KAEs) can enable earlier detection and better management of juvenile idiopathic arthritis (JIA). In this paper, we expand on our previous work by validating the KneeMS wearable for individuals with JIA in a clinical setting against a benchtop system (Dytran). Methods and procedures: Acoustic features were recorded from the medial and lateral sides of the patellar tendon using both Dytran and KneeMS in 36 participants with JIA during flexion/extension (FE). We calculated the Spearman correlation coefficient (ρ) between the acoustic features of both devices Acoustic features were recorded from the medial and lateral sides of the patellar tendon using both Dytran and KneeMS in 36 participants with JIA during flexion/extension (FE). We calculated the Spearman correlation coefficient between the acoustic features of both devices and assessed severity trends and longitudinal tracking of JIA knees from a machine learning (ML) pipeline differentiating active from inactive JIA knees. Results: Of 36 extracted features, six had the highest ρ (medial/lateral): RMS (0.79/0.70), MFCC1 (0.79/0.72), spectral slope (0.78/0.68), energy (0.78/0.67), Hjorth activity (0.75/0.71), and spectral flux (0.74/0.69). Our ML pipeline demonstrated an area under the receiver operating characteristic curve (AUC-ROC) mean of 0.77 (p<0.05) and showed a significant (p<0.05) upward trend in active knee prediction probabilities P(A) with clinical severity using KneeMS. For follow-ups, KneeMS P(A) moved in the clinically expected direction for 4 of 5 status-changing knees (versus 1 of 5 for Dytran); the clinical JADAS (cJADAS) score, available for most knees, was consistent with this finding. Conclusion: The high correlation between the acoustic features of Dytran and KneeMS, a significant AUC-ROC, significant JIA severity trend, and prospective longitudinal tracking make KneeMS a viable alternative to Dytran. Clinical impact: This validation establishes the foundation of home-based longitudinal joint assessment for JIA. Clinical and Translational Impact Statement: Wearable acoustic monitoring could enable at-home tracking of JIA disease activity, supporting timely treatment.

Chronic respiratory and heart diseases significantly impact the health and wellbeing of hundreds of millions of people worldwide. Wearable, continuous at-home monitoring could improve patient outcomes via early detection and monitoring of adverse incidents. Bioimpedance (BioZ) signals such as impedance pneumography (IP) and cardiac impedance plethysmography (cIPG) could enable continuous monitoring of both pulmonary and cardiac health. Validating these signals in compact electrode configurations can help inform design tradeoffs in wearable device form factors and provide insights into optimal placement on the body. In this work, we present SCRIBE: a compact, flexible, quad-modal chest patch that concurrently measures cardiac and respiratory mechanics using electrocardiography (ECG), seismocardiography (SCG), photoplethysmography (PPG), and BioZ. The 9.6 cm × 5 cm four-electrode array was validated against reference benchtop devices and acquires high-quality cIPG and IP signals in parallel with single-lead ECG at two central locations: the sternum and left subclavicle. Signal quality at peripheral subpectoral and midaxillary line placements was also examined. Using an established respiration signal quality index (SQI) pipeline, we demonstrate at least 85% of patch IP breaths are of high quality at the sternum or beneath the clavicle and can be mapped 1-1 with high quality spirometer breaths. Tidal volume (TV) estimated from patch-based IP signals were validated and predict TV within a Mean Absolute Percentage Error (MAPE) of 12.5% using less than 2 minutes of spirometry calibration data per subject, enabling short calibration times. Respiration rate (RR) estimation is also reported. SCRIBE can acquire high quality SCG, cIPG, and multi-wavelength (green/red/IR) reflectance PPG from the sternum, which was confirmed using Dynamic Time Warping (DTW) and Dynamic Time Feature Mapping (DTFM) SQI algorithms. At this location, more than 92% of all SCG, cIPG, and green/red/infrared PPG beats were high-quality. This is comparable to 95% and 99% of SCG and ICG beats from reference instruments. ECG-derived heart rate (HR) and PPG/cIPG pulse rate (PR) were also examined. Across all locations, the sternum confers the best combination of cardiac and respiratory signal quality, with subclavicle placement as a viable alternative. At both locations, SCRIBE enables continuous estimation of TV, RR, and cardiac timings for potential early detection of both exacerbated breathing events and increased risk of cardiovascular events.

A properly functioning cardiopulmonary system is essential for sustaining life, but can be compromised by acute exacerbations of cardiopulmonary conditions or progressive declines caused by degenerative diseases. Continuous cardiopulmonary monitoring has been proposed to improve the treatment of such impairments by enhancing triage in acute settings and facilitating the early detection and ongoing management of progressive disorders. In spite of this, suitable monitoring technologies are scarce. Existing systems often provide only coarse indicators of cardiopulmonary status or are too obtrusive for continuous use. We present reSPIRE, a chest-worn sensing system that acquires (S)eismocardiography, (P)hotoplethysmography, (I)mpedance pneumography and cardiography, surface (R)espiratory mechanomyography and electromyography, and (E)lectrocardiography signals. We validated the system on healthy participants (n=18) with controlled breathing maneuvers, incremental inspiratory and expiratory loading, and stationary cycling. Strong correlations were observed between wearable-derived indices of respiratory muscle force and inspiratory mouth pressure (Spearman: ρ=0.87, repeated measures: rrm=0.76). Significant phase-specific differences (p<0.001) in respiratory mechanomyography and electromyography band-powers were detected across both inspiratory and expiratory loading, leveraging respiratory phase context from the impedance pneuomgraphy signal. Tidal volume was accurately estimated from impedance pneumography, coefficient of determination (R2)=0.91, during sequences of spontaneous breathing and breathing while modulating rate and depth. Lastly, continuous tracking of hemodynamic (heart rate, pre-ejection period, impedance cardiography amplitude) and ventilatory changes was demonstrated throughout periods of cycling and recovery. These results demonstrate that the reSPIRE system's multimodal fusion enables precise monitoring of cardiopulmonary markers, advancing wearable-based monitoring and supporting its use in research of cardiopulmonary health.

Objective: Total joint arthroplasty (TJA) effectively treats end-stage hip and knee joint diseases, improving patients' quality of life. However, 1-2% of TJA patients develop prosthetic joint infections (PJI), which are challenging to diagnose and treat. This study investigates non-invasive active vibration and passive acoustic emission analyses for PJI monitoring. Methods: In this ex vivo study, periprosthetic joint effusions were simulated in seven cadaveric specimens with knee replacements by injecting saline and bacterial solutions into the joint space. Active sensing involved non-invasively stimulating the tibia with a miniature shaker, while passive sensing used manual stress to induce vibrations. Wideband, low-noise accelerometers captured the resulting vibrations, with spectral and temporal features extracted from the active and passive recordings, respectively. A qualitative analytical beam model of the knee-tibia system was developed to represent the fluid as structural changes at the boundary of the system. Results: Both methods proved to be sensitive to the fluid in the joint space. Linear regression models were built using the most informative features, estimating fluid volume with Pearson's r of 0.79 and mean absolute errors of 11.1 mL (active) and 11.9 mL (passive). Trends in frequency and time signals were consistent between the experimental results and the analytical model. Conclusion: The results of this study demonstrated the utility of novel vibration-based techniques to monitor periprosthetic joint effusions. Significance: These non-invasive techniques can lead to wearable devices for joint health monitoring, enabling PJI detection and personalized treatment plans, potentially improving patient outcomes and reducing PJI-related healthcare costs.

Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a cohort that no longer reflects contemporary critical care. No alternative learned directly from patient trajectories is in routine use. We conducted a retrospective two-cohort study on a total of 29,116 and 7,691 adult patients meeting Sepsis-3 criteria from two hospital systems in Massachusetts and Georgie, respectively. We developed a sepsis index using 43 routinely charted variables over a 72-hour treatment window. Unlike previous studies, we use mortality as a treatment-level ranking signal rather than a per-state target, allowing credit to be redistributed non-uniformly across timesteps. Evaluation was done on a permanent 20 Our index demonstrated hourly prognostic information that meaningfully separates patient outcomes and is consistent with clinical expectation, indicating potential as a decision support tool complementing clinical judgement.

Objective. We investigated (i) if blood volume decompensation status (BVDS) can be trend-tracked by hemodynamic parameters, and (ii) if hemodynamic parameters capable of trend-tracking BVDS can be trend-tracked by the physio-markers derived from the physiological signals measured using wearable sensors.Approach. In 9 pigs undergoing controlled hemorrhage and blood transfusion, we measured gold standard arterial blood pressure (BP), heart rate (HR), stroke volume (SV), and cardiac output (CO) via invasive aortic BP and flow signals. In addition, we derived non-invasive physio-markers from the electrocardiogram, photoplethysmogram (PPG), and seismocardiogram signals measured using wearable sensors. Then, we determined the best hemodynamic parameters to trend-track BVDS by comparing their correlation with BVDS. Finally, we investigated the feasibility of trend-tracking BVDS via non-invasive physio-markers in terms of their correlation with hemodynamic parameters as well as BVDS.Main results. SV and CO could trend-track BVDS more consistently and explainably than BP and HR during hemorrhage and blood transfusion. The physio-markers of SV (the ratio between left ventricular ejection time (LVET) and pre-ejection period (PEP): LVET/PEP and PPG amplitude:APPG) and CO (HR·LVET/PEP and HR·APPG) showed close and monotonic relationships to SV (LVET/PEP: Spearman correlation 0.96 (0.93-0.98) and Pearson correlation 0.96 (0.93-0.98)) and CO (HR·LVET/PEP: Spearman correlation 0.95 (0.91-0.97) and Pearson correlation 0.91 (0.89-0.97)), and they likewise showed close and monotonic relationships to BVDS. However, substantial inter-individual variability in the hemodynamic parameters and their physio-markers was also observed.Significance. These findings suggest the feasibility of wearable-enabled hemodynamic monitoring during hemorrhage and blood transfusion, as well as the challenges therein.

OBJECTIVE:Memory function underlies mental and behavioral health. While the role of the central nervous system (CNS) during episodic memory encoding and retrieval is well researched, the interplay between the CNS and the autonomic nervous system (ANS) during these processes is not. We addressed this gap by analyzing fluctuations in CNS-ANS network coupling during an episodic memory task (EMT). METHODS:Sixty-seven healthy adults completed an EMT consisting of three stages: Encoding, Retrieval 1, and Retrieval 2. From electroencephalogram (EEG) recordings, we estimated time-varying CNS indices via alpha and theta power. Simultaneously, from electrocardiogram (ECG) and seismocardiogram (SCG) recordings, we estimated time-varying parasympathetic and sympathetic indices via cardiac vagal index (CVI) and pre-ejection period, respectively (PEP). Using time delay stability and surrogate data analysis, for each protocol condition, we assessed coverage, the percentage of significant coupling links, over the CVI-Neural and PEP-Neural networks. RESULTS:Median coverage of CVI-Neural and PEP-Neural increased from Baseline to Encoding (+13.2%, $p$<0.001 and +7.9%, $p$<0.001), decreased from Encoding to Retrieval 1 (-5.3%, $p$<0.05 and -5.3%, $p$<0.001), and increased from Retrieval 1 to Retrieval 2 (+5.3%, $p$<0.001 and +5.3%, $p$<0.05). CONCLUSION:This is the first work to integrate EEG, ECG, and SCG time series to elucidate CNS-ANS interactions during an EMT. We demonstrated that healthy adults experienced parallel changes in CNS-parasympathetic and CNS-sympathetic network coverage during the task. SIGNIFICANCE:In the future, measuring neural and cardiomechanical signals to estimate CNS-ANS coupling while probing memory function in clinical populations may help derive new mental health screening biomarkers.

Sepsis heterogeneity reflects diverse etiologies and patient-specific physiological responses, motivating phenotype identification to enable precision therapeutics. However, most phenotyping approaches rely on intermittently sampled clinical variables, whereas continuously recorded physiological waveforms remain underutilized. We developed a deep-learning framework to derive physiological phenotypes from five-minute pre-onset electrocardiogram, photoplethysmogram and respiratory-impedance waveforms in 2,174 ICU patients meeting Sepsis-3 criteria. From these signals, 192 cardiorespiratory physiomarkers were extracted and embedded using a Feature Tokenizer Transformer encoder, which outperformed alternative representation methods. Consensus clustering identified four stable sepsis physio-phenotypes (SP-1-SP-4) associated with distinct autonomic and peripheral vascular signatures. Despite similar baseline severity and demographics, phenotypes differed significantly in mortality (19-29%), septic shock, vasopressor use and mechanical ventilation, with divergent 28-day survival trajectories (P<0.01). Explainable AI provided clinically interpretable characterizations, and a trained classifier enabled real-time bedside phenotyping. This framework establishes waveform-based phenotyping as a foundation for precision medicine in sepsis care.

Timely assessment of blood volume decompensation status (BVDS) is essential for effective trauma intervention, especially in prehospital and resource-limited environments. Prior work has shown that BVDS models can be developed for hypovolemia datasets in large animals (pigs), but the ability of these models to generalize across data collections, sites, and specific implementations of the sensor hardware has not yet been evaluated. In this work, we leveraged a recently collected dataset where a new self-contained wearable device was used for data collection, together with transfer learning, to evaluate and optimize generalizability of the BVDS estimation algorithm.We analyze two controlled-hemorrhage datasets that include synchronized electrocardiogram, seismocardiogram, and photoplethysmogram signals collected using (i) a research-grade, non-ambulatory monitor and (ii) a US Food and Drug Administration (FDA)-cleared wearable patch sensor. We evaluate several machine learning approaches for cross-device BVDS estimation, including multilayer perceptrons (MLPs) and tree-based regressors. To enable knowledge transfer, we implement and compare three transfer learning strategies for the datasets: freezing the network weights and fine-tuning only the decision layer, fine-tuning the network end-to-end without freezing, and augmenting a pretrained gradient boosting tree model (e.g., XGBoost) by adding additional trees.Models trained solely on wearable data achieved root mean square errors (RMSEs) for BVDS, as compared to reference standard values derived from invasive catheter-based pressures, of 23.01% and 20.95%, while fine-tuning with non-ambulatory data reduced errors to 22.79% and 18.71%, respectively for MLP and XGBoost. By establishing a data-efficient transfer learning framework, this work offers a practical pathway toward wearable BVDS monitoring in the field with data and label scarcity by demonstrating the efficacy of knowledge transfer between different datasets collected under controlled hemorrhage conditions.

Acute psychological stress has a multifaceted impact on cardiovascular physiology, including increases in chronotropy, inotropy, and vascular tone. Chronic exposure to stress may greatly increase cardiovascular risk. To examine the cardiovascular impact of acute stress comprehensively, new multimodal portable monitoring solutions are needed. We examined the feasibility of using a compact multimodal wearable patch to measure laboratory-based stress-induced cardiovascular responses in a diverse sample with recent myocardial infarction (MI) and healthy participants (N = 37, 28 MI) during a protocol with a public-speaking stressor. Using the electrocardiogram, seismocardiogram, and photoplethysmogram captured from the device, we found several significant (p < 0.05) autonomic changes in the pooled sample suggesting stress activation: increases in heart rate, chest photoplethysmogram amplitude, and perfusion index and decreases in heart rate variability, left ventricular ejection time, pulse arrival time, and pulse transit time. We, thus, demonstrated that a single wearable device can capture stress-induced cardiovascular changes, enabling simultaneous examination of stress-induced inotropic, chronotropic, and vascular effects. This portable, wireless chest patch may be useful in comprehensively and unobtrusively examining stress-induced cardiovascular effects in-lab. Given the public health importance of psychological stress and cardiovascular disease, future studies should assess the device's full clinical potential in larger groups with longer monitoring periods.

Cardiovascular arousal to acute mental stressors (AMSs) poses significant risks to health and contributes to cardiovascular and neurological disorders. Transcutaneous median nerve stimulation (TMNS) is an emerging noninvasive intervention with the potential to enable just-in-time mitigation of cardiovascular arousal to acute mental stressors. However, no prior work has addressed how to best control TMNS for this purpose. A critical limitation is the lack of basic knowledge of the dynamics between acute mental stressors and TMNS versus cardiovascular arousal. As an initial step toward investigating closed-loop controlled just-in-time TMNS for mitigating cardiovascular arousal to acute mental stressors, we developed and evaluated a data-driven virtual experiment generator (VEG) which can replicate the dynamics between acute mental stressors and TMNS versus cardiovascular arousal. In terms of a novel synthetic multimodal variable (SMV) intended to serve as feedback for just-in-time TMNS, we derived a fourth-order linear decoupled multi-input-single-output (MISO) representation to replicate the dynamics between acute mental stressors and TMNS as inputs versus SMV as output using data collected from 23 experiments. The representation could (i) replicate SMV responses in all experiments when parameterized with the experiment-specific parametric probability density functions (PDFs) and (ii) generate plausible virtual experiments when parameterized with the experiment-aggregated parametric PDFs. In sum, the VEG has the potential as a virtual platform to develop and evaluate closed-loop controlled just-in-time TMNS for mitigating acute mental stressor-induced cardiovascular arousal with SMV as feedback.

Objective Posttraumatic Stress Disorder (PTSD) is a highly prevalent condition, and current treatments have limitations. Vagal Nerve Stimulation (VNS) is a new approach that potentially has promise for PTSD. Understanding the neurobiology of treatment response is important for developing new treatments. The purpose of this study was to assess neural correlations of long-term transcutaneous cervical VNS (tcVNS) in patients with PTSD. Methods Patients with PTSD underwent randomization to active tcVNS (N=6) or sham stimulation (N=5) twice daily for three months. High-Resolution Positron Emission Tomography scanning with radiolabeled water was used to measure brain blood flow measurements before and after treatment during exposure to personalized traumatic scripts paired with active or sham stimulation. Results Three months of active tcVNS resulted in activation in response to traumatic scripts in the sham stimulation group not seen in the tcVNS group in brain areas mediating the fear response, including posterior cingulate, thalamus, temporal and parietal cortex, and parahippocampal gyrus, with an increase in medial prefrontal cortex with tcVNS, in patients with PTSD. Conclusion TcVNS affects brain areas mediating fear and emotion which may underlie a therapeutic effect for PTSD.