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.

Opioid use disorder (OUD) is a significant global health issue, leading to severe physiological and psychological impacts and substantial societal costs. Current methods for assessing opioid withdrawal, primarily relying on subjective scales, suffer from limitations such as incomplete symptom capture, recall bias, and imprecision. Wearable sensor technologies offer a promising alternative for objective assessment, with previous studies demonstrating their ability to detect opioid use and measure related physiological changes. In this study we investigated the correlation between local cardio-mechanical variability quantified using dynamic time warping (DTW) distances of seismocardiogram (SCG) signals and subjective opioid withdrawal severity (SOWS) scores. In a 7-day in-patient protocol for individuals with OUD$(N=13)$, we found a statistically significant inverse correlation: shorter median DTW distances and reduced variance in SCG signals were associated with higher subjective withdrawal scores with statistically significant differences between the highest withdrawal bin and the two lowest bins ($\mathbf{p}=0.038$and$\mathbf{p}=0.044$, respectively). Our results suggests that local cardio-mechanical variability, as captured by wearable sensors and analyzed with DTW, can serve as a valuable indicator for quantifying opioid withdrawal severity, potentially enabling more timely and effective preventive care.

Physiological markers of stress and neuromodulation (e.g., heart rate variability) are often inconsistent when it comes to quantifying changes in autonomic nervous system function. This inconsistency is explained by the autonomic nervous system's output varying across organ systems, as well as limitations in what each marker quantifies. In this work, we present an unsupervised learning approach we term StressFADS: Stress Factor Analysis via Dynamical Systems. StressFADS overcomes single marker inconsistencies by learning underlying dynamics that are shared across physiological markers of stress. StressFADS's encoder summarizes a time window of physiological markers and initializes a recurrent neural network (RNN) with this summary. This RNN is autonomously simulated forward in time, and the output at each timestep is fed through a dimension-ality reduction stage trained to reconstruct the original window of physiological markers. This forces the model to learn latent representations that capture shared dynamics across the markers. We apply StressFADS to the analysis of approximately 50 hours of 1-Hz cardiovascular and respiratory marker time series from a double-blind, randomized controlled trial (N = 26) involving trauma recall and active or sham cervical transcutaneous vagus nerve stimulation (tVNS). We find that StressFADS learned latent factors that successfully quantify differences between stress induced by trauma recall, a neutral condition, and active or sham tVNS. This is promising and motivates future work in learning latent autonomic states that more faithfully track changes in stress and intervention effects for just-in-time stress mitigation.

BACKGROUND: Posttraumatic stress disorder (PTSD) causes heightened fight-or-flight responses to traumatic memories (i.e., hyperarousal). Although hyperarousal is hypothesized to cause irregular breathing (i.e., respiratory variability), no quantitative markers of respiratory variability have been shown to correspond with PTSD symptoms in humans. OBJECTIVE: In this study, we define interpretable markers of respiration pattern variability (RPV) and investigate whether these markers respond during traumatic memories, correlate with PTSD symptoms, and differ in patients with PTSD. METHODS: We recruited 156 veterans from the Vietnam-Era Twin Registry to participate in a trauma recall protocol. From respiratory effort and electrocardiogram measurements, we extracted respiratory timings and rate using a robust quality assessment and fusion approach. We then quantified RPV using the interquartile range and compared RPV between baseline and trauma recall conditions, correlated PTSD symptoms to the difference between trauma recall and baseline RPV (i.e., ∆RPV), and compared ∆RPV between patients with PTSD and trauma-exposed controls. Leveraging a subset of 116 paired twins, we then uniquely controlled for factors shared by co-twins via within-pair analysis for further validation. RESULTS: We found RPV was increased during traumatic memories (p .001), ∆ RPV was positively correlated with PTSD symptoms (p .05), and patients with PTSD exhibited higher ∆ RPV than trauma-exposed controls (p . 05). CONCLUSIONS: This paper is the first to elucidate RPV markers that respond during traumatic memories, especially in patients with PTSD, and correlate with PTSD symptoms. SIGNIFICANCE: These findings encourage future studies outside the clinic, where interpretable markers of respiratory variability are used to track hyperarousal.

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.

Transcutaneous cervical vagal nerve stimulation (tcVNS) devices are attractive alternatives to surgical implants, and can be applied for a number of conditions in ambulatory settings, including stress-related neuropsychiatric disorders. Transferring tcVNS technologies to at-home settings brings challenges associated with the assessment of therapy response. The ability to accurately detect whether tcVNS has been effectively delivered in a remote setting such as the home has never been investigated. We designed and conducted a study in which 12 human subjects received active tcVNS and 14 received sham stimulation in tandem with traumatic stress, and measured continuous cardiopulmonary signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), and respiratory effort (RSP). We extracted physiological parameters related to autonomic nervous system activity, and created a feature set from these parameters to: 1) detect active (vs. sham) tcVNS stimulation presence with machine learning methods, and 2) determine which sensing modalities and features provide the most salient markers of tcVNS-based changes in physiological signals. Heart rate (ECG), vasomotor activity (PPG), and pulse arrival time (ECG+PPG) provided sufficient information to determine target engagement (compared to sham) in addition to other combinations of sensors. resulting in 96% accuracy, precision, and recall with a receiver operator characteristics area of 0.96. Two commonly utilized sensing modalities (ECG and PPG) that are suitable for home use can provide useful information on therapy response for tcVNS. The methods presented herein could be deployed in wearable devices to quantify adherence for at-home use of tcVNS technologies.

Transcutaneous vagus nerve stimulation (t-VNS) is a promising technology for modulating brain function and possibly treating disorders of the central nervous system. While handheld devices are available for t-VNS, stimulation efficacy can only be quantified using expensive imaging or blood biomarker analyses. Additionally, the parameters and "dosage" recommendations for t-VNS are typically fixed, as there are limited biomarkers that can assess downstream effects of the stimulation outside of clinical settings. In this proof-of-concept study, we evaluated non-invasive peripheral cardiovascular measurements as physiological biomarkers of t-VNS efficacy. Specifically, we hypothesized two physiological biomarkers: (1) the pre-ejection period (PEP) of the heart - a parameter closely linked to sympathetic tone - and (2) the amplitude of peripheral photoplethysmogram (PPG) waveforms - representing changes in vasomotor tone and thus parasympathetic / sympathetic activation. A total of six healthy human subjects participated in the multi-day study, half each undergoing active or sham t-VNS stimulus. The three subjects receiving t-VNS had no decrease in PEP and an increase in PPG amplitude following t-VNS, while the subjects receiving sham stimulus had a decrease in PEP and no change in PPG amplitude. When combined with mental stress (a traumatic script being read back to the subjects), the group with t-VNS had no decrease in PEP and only a slight decrease in PPG amplitude following stimulus, while the group receiving sham stimulus had a decrease in PEP and also a slight decrease in PPG amplitude. These studies suggest that PEP and PPG amplitude measures may provide non-invasive physiological biomarkers of t-VNS efficacy, including in the presence of mental stress.