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.

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.

Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.

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.

Continuous remote monitoring of key health parameters can be facilitated by noninvasive cardiovascular signals such as the seismocardiogram (SCG) and electrocardiogram (ECG). However, the accuracy of health parameter estimation is dependent on the quality of the signals collected and the algorithms used, which can be particularly challenging in the presence of environmental noise and motion artifacts. In this work, SeismoNet, a highly-sensitive, low-power, multi-node wireless wearable platform is introduced that enables recording of small amplitude acceleration signals (especially SCG signals) and ECG from multiple points on the human body. Using the SeismoNet, we performed a study involving 20 human participants with five nodes placed around the trunk, and showed that combining multiple nodes can improve the estimation performance of heart rate (HR) and respiration rate (RR) by approximately 30% and 23%, respectively. This suggests that combining acceleration data from multiple points on the body can enhance the cardiac and respiratory content of the acquired data, resulting in more accurate predictions. Furthermore, the SeismoNet platform and the dataset collected can be utilized to explore research questions related to multi-point physiological sensing.

This paper presents algorithms to improve the estimation of heart rate (HR) and heart rate variability (HRV) from smartphone video. The remote photoplethysmogram (rPPG) signals are first extracted from the videos recorded. Next, we proposed an rPPG filter adaptively tuned by HR and respiratory rate (RR) to better enhance source signal that modulates HR. Additionally, we also addressed a unique smartphone artifact—occasionally seen in smartphone videos—by introducing a threshold-based algorithm. HR and HRV accuracies are assessed on 22 subjects who were instructed to breath at seven different RRs. The mean absolute errors of HR and standard deviation of the NN intervals (SDNN) are found to be 1.13 ± 0.68 bpm and 18.30 ± 10.33 ms respectively. Finally, we also conduct a few experiments to highlight the accuracy improvements made by the proposed algorithms.

OBJECTIVE: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. METHODS: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. MAJOR RESULTS: ) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. CONCLUSION: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. SIGNIFICANCE: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.