Fusing Near-Infrared Spectroscopy with Wearable Hemodynamic Measurements Improves Classification of Mental Stress.

Human-computer interaction (HCI) technology, and the automatic classification of a person's mental state, are of interest to multiple industries. In this work, the fusion of sensing modalities that monitor the oxygenation of the human prefrontal cortex (PFC) and cardiovascular physiology was evaluated to differentiate between rest, mental arithmetic and N-back memory tasks. A flexible headband to measure near-infrared spectroscopy (NIRS) for quantifying PFC oxygenation, and forehead photoplethysmography (PPG) for assessing peripheral cardiovascular activity was designed. Physiological signals such as the electrocardiogram (ECG) and seismocardiogram (SCG) were collected, along with the measurements obtained using the headband. The setup was tested and validated with a total of 16 human subjects performing a series of arithmetic and N-back memory tasks. Features extracted were related to cardiac and peripheral sympathetic activity, vasomotor tone, pulse wave propagation, and oxygenation. Machine learning techniques were utilized to classify rest, arithmetic, and N-back tasks, using leave-one-subject-out cross validation. Macro-averaged accuracy of 85%, precision of 84%, recall rate of 83%, and F1 score of 80% were obtained from the classification of the three states. Statistical analyses on the subject-based results demonstrate that the fusion of NIRS and peripheral cardiovascular sensing significantly improves the accuracy, precision, recall, and F1 scores, compared to using NIRS sensing alone. Moreover, the fusion significantly improves the precision compared to peripheral cardiovascular sensing alone. The results of this work can be used in the future to design a multi-modal wearable sensing system for classifying mental state for applications such as acute stress detection.

Improving Respiratory Timing Estimation Using Quality Indexing and Electrocardiogram-Derived Respiration.

Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time (Te)., In a sample of 15 healthy human participants undergoing a protocol of five controlled breathing exercises in four postures each, electrocardiogram (ECG) and RSP signals were collected. RSP signals were processed to extract breath-by-breath estimates of RR, Ti, and Te. These estimates were compared against ground truth spirometry-based estimates using Bland-Altman analysis. We find that incorporating quality indexing and outlier removal prior to feature extraction improves the 95% limits of agreement by 10-40%. We also find that by using ECG-derived respiration (EDR) during periods of RSP artifact, the data removal necessary for accurate respiratory timing estimation is significantly reduced ( for all postures). These findings encourage the use of quality assessment and EDR to enhance the robustness of RR, Ti, and Te estimation from RSP signals. Clinical Relevance- Detecting stimulus-induced or pathological changes in respiratory function can enhance our understanding and monitoring of respiratory health. Quality assessment and the use of EDR help accomplish this by enabling more accurate measurement of respiratory timings.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed.

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Accurate Ballistocardiogram Based Heart Rate Estimation Using an Array of Load Cells in a Hospital Bed.

The ballistocardiogram (BCG), a cardiac vibration signal, has been widely investigated for continuous monitoring of heart rate (HR). Among BCG sensing modalities, a hospital bed with multi-channel load-cells could provide robust HR estimation in hospital setups. In this work, we present a novel array processing technique to improve the existing HR estimation algorithm by optimizing the fusion of information from multiple channels. The array processing includes a Gaussian curve to weight the joint probability according to the reference value obtained from the previous inter-beat-interval (IBI) estimations. Additionally, the probability density functions were selected and combined according to their reliability measured by q-values. We demonstrate that this array processing significantly reduces the HR estimation error compared to state-of-the-art multi-channel heartbeat detection algorithms in the existing literature. In the best case, the average mean absolute error (MAE) of 1.76 bpm in the supine position was achieved compared to 2.68 bpm and 1.91 bpm for two state-of-the-art methods from the existing literature. Moreover, the lowest error was found in the supine posture (1.76 bpm) and the highest in the lateral posture (3.03 bpm), thus elucidating the postural effects on HR estimation. The IBI estimation capability was also evaluated, with a MAE of 16.66 ms and confidence interval (95%) of 38.98 ms. The results demonstrate that improved HR estimation can be obtained for a bed-based BCG system with the multi-channel data acquisition and processing approach described in this work.

Enabling Wearable Pulse Transit Time-Based Blood Pressure Estimation for Medically Underserved Areas and Health Equity: Comprehensive Evaluation Study.

BACKGROUND: Noninvasive and cuffless approaches to monitor blood pressure (BP), in light of their convenience and accuracy, have paved the way toward remote screening and management of hypertension. However, existing noninvasive methodologies, which operate on mechanical, electrical, and optical sensing modalities, have not been thoroughly evaluated in demographically and racially diverse populations. Thus, the potential accuracy of these technologies in populations where they could have the greatest impact has not been sufficiently addressed. This presents challenges in clinical translation due to concerns about perpetuating existing health disparities. OBJECTIVE: In this paper, we aim to present findings on the feasibility of a cuffless, wrist-worn, pulse transit time (PTT)-based device for monitoring BP in a diverse population. METHODS: We recruited a diverse population through a collaborative effort with a nonprofit organization working with medically underserved areas in Georgia. We used our custom, multimodal, wrist-worn device to measure the PTT through seismocardiography, as the proximal timing reference, and photoplethysmography, as the distal timing reference. In addition, we created a novel data-driven beat-selection algorithm to reduce noise and improve the robustness of the method. We compared the wearable PTT measurements with those from a finger-cuff continuous BP device over the course of several perturbations used to modulate BP. RESULTS: Our PTT-based wrist-worn device accurately monitored diastolic blood pressure (DBP) and mean arterial pressure (MAP) in a diverse population (N=44 participants) with a mean absolute difference of 2.90 mm Hg and 3.39 mm Hg for DBP and MAP, respectively, after calibration. Meanwhile, the mean absolute difference of our systolic BP estimation was 5.36 mm Hg, a grade B classification based on the Institute for Electronics and Electrical Engineers standard. We have further demonstrated the ability of our device to capture the commonly observed demographic differences in underlying arterial stiffness. CONCLUSIONS: Accurate DBP and MAP estimation, along with grade B systolic BP estimation, using a convenient wearable device can empower users and facilitate remote BP monitoring in medically underserved areas, thus providing widespread hypertension screening and management for health equity.

Automatic Detection of Target Engagement in Transcutaneous Cervical Vagal Nerve Stimulation for Traumatic Stress Triggers.

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.

Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stress.

BACKGROUND: Stress is associated with activation of the sympathetic nervous system, and can lead to lasting alterations in autonomic function and in extreme cases symptoms of posttraumatic stress disorder (PTSD). Vagal nerve stimulation (VNS) is a potentially useful tool as a modulator of autonomic nervous system function, however currently available implantable devices are limited by cost and inconvenience. OBJECTIVE: The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to stress. METHODS: Using a double-blind approach, we investigated the effects of active or sham tcVNS on peripheral cardiovascular and autonomic responses to stress using wearable sensing devices in 24 healthy human participants with a history of exposure to psychological trauma. Participants were exposed to acute stressors over a three-day period, including personalized scripts of traumatic events, public speech, and mental arithmetic tasks. RESULTS: tcVNS relative to sham applied immediately after traumatic stress resulted in a decrease in sympathetic function and modulated parasympathetic/sympathetic autonomic tone as measured by increased pre-ejection period (PEP) of the heart (a marker of cardiac sympathetic function) of 4.2 ms (95% CI 1.6-6.8 ms, p < 0.01), decreased peripheral sympathetic function as measured by increased photoplethysmogram (PPG) amplitude (decreased vasoconstriction) by 47.9% (1.4-94.5%, p < 0.05), a 9% decrease in respiratory rate (-14.3 to -3.7%, p < 0.01). Similar effects were seen when tcVNS was applied after other stressors and in the absence of a stressor. CONCLUSION: Wearable sensing modalities are feasible to use in experiments in human participants, and tcVNS modulates cardiovascular and peripheral autonomic responses to stress.

Transcutaneous cervical vagal nerve stimulation reduces sympathetic responses to stress in posttraumatic stress disorder: A double-blind, randomized, sham controlled trial.

OBJECTIVE: Exacerbated autonomic responses to acute stress are prevalent in posttraumatic stress disorder (PTSD). The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on autonomic responses to acute stress in patients with PTSD. The authors hypothesized tcVNS would reduce the sympathetic response to stress compared to a sham device. METHODS: Using a randomized double-blind approach, we studied the effects of tcVNS on physiological responses to stress in patients with PTSD (n = 25) using noninvasive sensing modalities. Participants received either sham (n = 12) or active tcVNS (n = 13) after exposure to acute personalized traumatic script stress and mental stress (public speech, mental arithmetic) over a three-day protocol. Physiological parameters related to sympathetic responses to stress were investigated. RESULTS: Relative to sham, tcVNS paired to traumatic script stress decreased sympathetic function as measured by: decreased heart rate (adjusted ß = -5.7%; 95% CI: ±3.6%, effect size d = 0.43, p < 0.01), increased photoplethysmogram amplitude (peripheral vasodilation) (30.8%; ±28%, 0.29, p < 0.05), and increased pulse arrival time (vascular function) (6.3%; ±1.9%, 0.57, p < 0.0001). Similar (p < 0.05) autonomic, cardiovascular, and vascular effects were observed when tcVNS was applied after mental stress or without acute stress. CONCLUSION: tcVNS attenuates sympathetic arousal associated with stress related to traumatic memories as well as mental stress in patients with PTSD, with effects persisting throughout multiple traumatic stress and stimulation testing days. These findings show that tcVNS has beneficial effects on the underlying neurophysiology of PTSD. Such autonomic metrics may also be evaluated in daily life settings in tandem with tcVNS therapy to provide closed-loop delivery and measure efficacy.ClinicalTrials.gov Registration # NCT02992899.

Transcutaneous vagal nerve stimulation blocks stress-induced activation of Interleukin-6 and interferon-γ in posttraumatic stress disorder: A double-blind, randomized, sham-controlled trial.

Posttraumatic stress disorder (PTSD) is a highly disabling condition associated with alterations in multiple neurobiological systems, including increases in inflammatory function. Vagus nerve stimulation (VNS) decreases inflammation, however few studies have examined the effects of non-invasive VNS on physiology in human subjects, and no studies in patients with PTSD. The purpose of this study was to assess the effects of transcutaneous cervical VNS (tcVNS) on inflammatory responses to stress. Thirty subjects with a history of exposure to traumatic stress with (N â€‹= â€‹10) and without (N â€‹= â€‹20) PTSD underwent exposure to stressful tasks immediately followed by active or sham tcVNS and measurement of multiple biomarkers of inflammation (interleukin-(IL)-6, IL-2, IL-1ß, Tumor Necrosis Factor alpha (TNFα) and Interferon gamma (IFNγ) over multiple time points. Stressful tasks included exposure to personalized scripts of traumatic events on day 1, and public speech and mental arithmetic (Mental Stress) tasks on days 2 and 3. Traumatic scripts were associated with a pattern of subjective anger measured with Visual Analogue Scales and increased IL-6 and IFNγ in PTSD patients that was blocked by tcVNS (p â€‹< â€‹.05). Traumatic stress had minimal effects on these biomarkers in non-PTSD subjects and there was no difference between tcVNS or sham. No significant differences were seen between groups in IL-2, IL-1ß, or TNFα. These results demonstrate that tcVNS blocks behavioral and inflammatory responses to stress reminders in PTSD.