Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Persons who have experienced a prior myocardial infarction (MI) are at a greater risk of experiencing a secondary event. External factors, such as stress, can cause transient hypertension, further increasing cardiovascular disease risk. However, challenges to monitoring stress-induced hypertension include obtrusiveness of monitoring devices, unknown validity in high-risk populations, and unsubstantiated sensitivity in stressful environments. In this pilot study, we investigated data from a validated, multimodal, wearable patch to examine physiological correlates of laboratory-based hypertensive stress responses. The device collected electrocardiogram, seismocardiogram, and photoplethysmogram signals from 35 participants (26 post-MI and 9 healthy participants) during a protocol involving a public speaking stressor. We calculated the change from rest to stress in 10 features extracted from these signals and assessed their correlation to stress-induced changes in mean arterial pressure (MAP) derived from a validated brachial pressure cuff. MAP changes correlated significantly with changes in heart rate (p<0.001, r=0.69), left ventricular ejection time (p<0.001, r=-0.63), pulse arrival time (p<0.001, r=-0.58), and pulse transit time (p<0.001, r=-0.56) captured by the patch.Clinical relevance— We demonstrate the potential of a multimodal patch to capture multiple physiological correlates of stress-induced hypertension with moderate dose-response effect sizes. Future work with larger populations could combine these physiological correlates for the purpose of cardiovascular risk stratification using low-burden technologies.

Induced craving frequently leads to relapse in patients with a history of opioid use disorder (OUD). Quantifying the physiological manifestations of craving can enable caregivers of patients with OUD to continuously monitor craving and thus help inform treatment plans. Heart rate variability (HRV) is a promising candidate to capture the trends in such manifestations due to the ease of measuring changes in heartbeat intervals using the electrocardiogram. However, the relationship between craving and HRV measured over periods shorter than 5 minutes, i.e., ultra-short-term HRV, has not yet been investigated in detail. In this work, we present the first analysis on the relationship between 12 HRV features computed over ~2-minute periods and subjective craving reported on the visual analog scale (VAS-craving). Patients with OUD stable on medication (N = 12) went through an approximately 2-hour protocol involving audio/visual opioid cues to induce craving, alongside active transcutaneous vagus nerve stimulation or sham stimulation. Regardless of stimulation type, changes in VAS-craving scores over the course of the protocol were negatively correlated with changes in two ultra-short-term HRV features: the number of adjacent normal heartbeat intervals differing by more than 50 milliseconds (r = -0.63, p = 0.03) and the total power across all frequency bands (r = -0.69, p = 0.03).Clinical relevance—This preliminary study suggests that certain ultra-short-term HRV features can potentially serve as indicators of craving in patients with a history of OUD. Using such metrics computed over small data intervals can enable efficient clinical decision making and may help prevent opioid relapse.

Acute stress has been linked to an increased risk for adverse cardiac events, necessitating the development of techniques for continuous stress monitoring and regulation. Wearable sensing can be used to continuously monitor cardiovascular signals relevant to acute stress. However, to enable real-time stress mitigation techniques in a wearable system, both cardiac and vascular features of acute stress need to be extracted from these signals in real-time. We have developed an efficient algorithm capable of accurately extracting heart rate (HR) and photoplethysmogram (PPG) amplitude (PPGamp) from PPG signals on resource-constrained wearable devices. This algorithm was deployed on a custom-designed wearable device and was compared against a reference electrocardiogram and standard signal processing techniques in a protocol involving acute stressors. The device consumed an average of 113 mA during the protocol. Through correlation and Bland-Altman analyses, we found that the features extracted using our algorithm were significantly (p<0.001) correlated (HR: r=0.919, PPGamp: r=0.993) and strongly agreed with (HR: <5 beats per minute difference, PPGamp: <5% difference at 95% limits of agreement) those derived using the benchtop devices and post-processing algorithms. These results demonstrate our algorithm's ability to extract both the cardiac and vascular effects of acute stress in real-time, enabling future work in the area of wearable closed-loop stress detection and mitigation.

Patients with prior myocardial infarction (MI) have an increased risk of experiencing a secondary event which is exacerbated by mental stress. Our team has developed a miniaturized patch with the capability to capture electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals which may provide multimodal information to characterize stress responses within the post-MI population in ambulatory settings. As ECG-derived features have been shown to be informative in assessing the risk of MI, a critical first step is to ensure that the patch ECG features agree with gold-standard devices, such as the Biopac. However, this is yet to be done in this population. We, thus, performed a comparative analysis between ECG-derived features (heart rate (HR) and heart rate variability (HRV)) of the patch and Biopac in the context of stress. Our dataset contained post-MI and healthy control subjects who participated in a public speaking challenge. Regression analyses for patch and Biopac HR and HRV features (RMSSD, pNN50, SD1/SD2, and LF/HF) were all significant (p<0.001) and had strong positive correlations (r>0.9). Additionally, Bland-Altman analyses for most features showed tight limits of agreement: 0.999 bpm (HR), 11.341 ms (RMSSD), 0.07% (pNN50), 0.146 ratio difference (SD1/SD2), 0.750 ratio difference (LF/HF).Clinical relevance— This work demonstrates that ECG-derived features obtained from the patch and Biopac are in agreement, suggesting the clinical utility of the patch in deriving quantitative metrics of physiology during stress in post-MI patients. This has the potential to improve post-MI patients' outcomes, but needs to be further evaluated.

Background:Opioid Use Disorder (OUD) is an escalating public health problem with over 100,000 drug overdose-related deaths last year most of them related to opioid overdose, yet treatment options remain limited. Non-invasive Vagal Nerve Stimulation (nVNS) can be delivered via the ear or the neck and is a non-medication alternative to treatment of opioid withdrawal and OUD with potentially widespread applications.Methods:This paper reviews the neurobiology of opioid withdrawal and OUD and the emerging literature of nVNS for the application of OUD. Literature databases for Pubmed, Psychinfo, and Medline were queried for these topics for 1982-present.Results:Opioid withdrawal in the context of OUD is associated with activation of peripheral sympathetic and inflammatory systems as well as alterations in central brain regions including anterior cingulate, basal ganglia, and amygdala. NVNS has the potential to reduce sympathetic and inflammatory activation and counter the effects of opioid withdrawal in initial pilot studies. Preliminary studies show that it is potentially effective at acting through sympathetic pathways to reduce the effects of opioid withdrawal, in addition to reducing pain and distress.Conclusions:NVNS shows promise as a non-medication approach to OUD, both in terms of its known effect on neurobiology as well as pilot data showing a reduction in withdrawal symptoms as well as physiological manifestations of opioid withdrawal.

Over 100,000 individuals in the United States lost their lives secondary to drug overdose in 2021, with opioid use disorder (OUD) being a leading cause. Pain is an important component of opioid withdrawal, which can complicate recovery from OUD. This study's objectives were to assess the effects of transcutaneous cervical vagus nerve stimulation (tcVNS), a technique shown to reduce sympathetic arousal in other populations, on pain during acute opioid withdrawal and to study pain's relationships with objective cardiorespiratory markers. Twenty patients with OUD underwent opioid withdrawal while participating in a two-hour protocol. The protocol involved opioid cues to induce opioid craving and neutral conditions for control purposes. Adhering to a double-blind design, patients were randomly assigned to receive active tcVNS (n = 9) or sham stimulation (n = 11) throughout the protocol. At the beginning and end of the protocol, patients' pain levels were assessed using the numerical rating scale (0–10 scale) for pain (NRS Pain). During the protocol, electrocardiogram and respiratory effort signals were measured, from which heart rate variability (HRV) and respiration pattern variability (RPV) were extracted. Pre- to post- changes (denoted with a Δ) were computed for all measures. Δ NRS Pain scores were lower (P = 0.045) for the active group (mean ± standard deviation: −0.8 ± 2.4) compared to the sham group (0.9 ± 1.0). A positive correlation existed between Δ NRS pain scores and Δ RPV (Spearman's ρ = 0.46; P = 0.04). Following adjustment for device group, a negative correlation existed between Δ HRV and Δ NRS Pain (Spearman's ρ = −0.43; P = 0.04). This randomized, double-blind, sham-controlled pilot study provides the first evidence of tcVNS-induced reductions in pain in patients with OUD experiencing opioid withdrawal. This study also provides the first quantitative evidence of an association between breathing irregularity and pain. The correlations between changes in pain and changes in objective physiological markers add validity to the data. Given the clinical importance of reducing pain non-pharmacologically, the findings support the need for further investigation of tcVNS and wearable cardiorespiratory sensing for pain monitoring and management in patients with OUD.

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson's would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson's disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson's disease dataset comprised of healthy-elderly, healthy-young and Parkinson's disease patients. Our code is available at https://github.com/itsmeafra/Sublevel-Set-TDA.