A Nonlinear Stochastic Filter for Continuous-Time State Estimation.

Nonlinear filters produce a nonparametric estimate of the probability density of state at each point in time. Currently-known nonlinear filters include Particle Filters and the Kushner equation (and its un-normalized version: the Zakai equation). However, these filters have limited measurement models: Particle Filters require measurement at discrete times, and the Kushner and Zakai equations only apply when the measurement can be represented as a function of the state. We present a new nonlinear filter for continuous-time measurements with a much more general stochastic measurement model. It integrates to Bayes' rule over short time intervals and provides Bayes-optimal estimates from quantized, intermittent, or ambiguous sensor measurements. The filter has a close link to Information Theory, and we show that the rate of change of entropy of the density estimate is equal to the mutual information between the measurement and the state and thus the maximum achievable. This is a fundamentally new class of filter that is widely applicable to nonlinear estimation for continuous-time control.

Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis.

BACKGROUND: A quantitative measurement of serum proteome biomarkers that would associate with disease progression endpoints can provide risk stratification for persons with multiple sclerosis (PwMS) and supplement the clinical decision-making process. MATERIALS AND METHODS: In total, 202 PwMS were enrolled in a longitudinal study with measurements at two time points with an average follow-up time of 5.4 years. Clinical measures included the Expanded Disability Status Scale, Timed 25-foot Walk, 9-Hole Peg, and Symbol Digit Modalities Tests. Subjects underwent magnetic resonance imaging to determine the volumetric measures of the whole brain, gray matter, deep gray matter, and lateral ventricles. Serum samples were analyzed using a custom immunoassay panel on the Olink™ platform, and concentrations of 18 protein biomarkers were measured. Linear mixed-effects models and adjustment for multiple comparisons were performed. RESULTS: Subjects had a significant 55.6% increase in chemokine ligand 20 (9.7 pg/mL vs. 15.1 pg/mL, p < 0.001) and neurofilament light polypeptide (10.5 pg/mL vs. 11.5 pg/mL, p = 0.003) at the follow-up time point. Additional changes in CUB domain-containing protein 1, Contactin 2, Glial fibrillary acidic protein, Myelin oligodendrocyte glycoprotein, and Osteopontin were noted but did not survive multiple comparison correction. Worse clinical performance in the 9-HPT was associated with neurofilament light polypeptide (p = 0.001). Increases in several biomarker candidates were correlated with greater neurodegenerative changes as measured by different brain volumes. CONCLUSION: Multiple proteins, selected from a disease activity test that represent diverse biological pathways, are associated with physical, cognitive, and radiographic outcomes. Future studies should determine the utility of multiple protein assays in routine clinical care.

Feasibility, Acceptability, and Performance of a Continuous Temperature Monitor in Older Adults and Staff in Congregate-Living Facilities.

OBJECTIVES: Residents of congregate-living facilities are susceptible to disability and mortality from infection given the presence of advanced age, multimorbidity, and frailty-as demonstrated in the recent COVID pandemic. This study assessed the feasibility, acceptability, and applicability of a continuous temperature monitoring device in a congregate-living facility with residents of independent living, assisted living, and their care-providing staff. We hypothesized that a wearable device compared with daily manual temperature assessment would be well tolerated and more effective at detecting temperature variances than current standard of care body temperature assessment. DESIGN: Feasibility study. SETTING AND PARTICIPANTS: Residents of assisted and independent living and staff of a retirement community. METHODS: Thirty-five participants, including residents in assisted- and independent-living facilities (25) and staff (10) were enrolled in a 90-day feasibility study and wore a continuous temperature sensor from March to July 2021. Primary outcomes included study completion, ability to reapply the sensor, temperature data acquisition, and data availability from the sensors. A secondary analysis of the temperature data involved comparing the method of obtaining temperature using the continuous monitoring device against standard of care using traditional manual thermometers. RESULTS: Overall, 91.3% of residents, who were in the study during the first reapplication, were able to apply the device without assistance (21 of 23), and 80% of resident participants completed the study (20 of 25). For staff participants, completion rates and reapplication rates were 100%. Data acquisition rates from the continuous temperature devices were much higher than manual temperatures. Four episodes of fever were detected by the devices; manual temperature checks did not identify these events. CONCLUSIONS AND IMPLICATIONS: Continuous temperature monitoring in an older adult population and the staff in congregate-living facilities is feasible and acceptable. This approach identified fever undetected by current standard of care indicating the capability of this device for earlier detection of fevers.

Automatic classification and robust identification of vestibulo-ocular reflex responses: from theory to practice: introducing GNL-HybELS.

The Vestibulo-Ocular Reflex (VOR) stabilizes images of the world on our retinae when our head moves. Basic daily activities are thus impaired if this reflex malfunctions. During the past few decades, scientists have modeled and identified this system mathematically to diagnose and treat VOR deficits. However, traditional methods do not analyze VOR data comprehensively because they disregard the switching nature of nystagmus; this can bias estimates of VOR dynamics. Here we propose, for the first time, an automated tool to analyze entire VOR responses (slow and fast phases), without a priori classification of nystagmus segments. We have developed GNL-HybELS (Generalized NonLinear Hybrid Extended Least Squares), an algorithmic tool to simultaneously classify and identify the responses of a multi-mode nonlinear system with delay, such as the horizontal VOR and its alternating slow and fast phases. This algorithm combines the procedures of Generalized Principle Component Analysis (GPCA) for classification, and Hybrid Extended Least Squares (HybELS) for identification, by minimizing a cost function in an optimization framework. It is validated here on clean and noisy VOR simulations and then applied to clinical VOR tests on controls and patients. Prediction errors were less than 1 deg for simulations and ranged from .69 deg to 2.1 deg for the clinical data. Nonlinearities, asymmetries, and dynamic parameters were detected in normal and patient data, in both fast and slow phases of the response. This objective approach to VOR analysis now allows the design of more complex protocols for the testing of oculomotor and other hybrid systems.

A Novel Wearable Device for Continuous Temperature Monitoring & Fever Detection.

Objective: Continuous temperature monitoring in high-risk patients can enable healthcare providers to remotely track patients' temperatures, promptly detect fevers and timely intervene to improve clinical outcomes. We evaluated if a novel wearable, continuous temperature monitor (Verily Patch) can reliably estimate body temperature and early detect fevers in an outpatient setting in patients at a high risk of febrile neutropenia (FN) who recently underwent chemotherapy and autologous stem cell transplantation (ASCT). Methods: 86 patients at a high risk for FN were prospectively enrolled at Mayo Clinic, MN. Patients wore the device in their axilla region for 7 days post ASCT and recorded self-measured oral temperatures every 3 hours. Patients were also followed using clinical standard-of-care procedures with daily oral temperature assessment. The clinic- and patient-assessed oral temperatures were used to develop and evaluate Verily Patch's body temperature and early fever detection algorithms using a K-fold cross-validation approach. Results: The Verily Patch reliably measured body temperatures with an error of 0.35 ± 0.88°F in comparison to clinic- and patient-assessed oral temperatures. The sensitivity and specificity of the patch in detecting clinic-assessed fever episodes was 90.2% and 87.8%. The patch detected 14.3 times the number of clinic-assessed fever episodes with a median lead time of 4.3 hours. Conclusion: Patient self-monitoring of temperature and fever incidents suffers from low accuracy and is impractical for extended periods of time. Continuous temperature monitoring by a wearable device (such as Verily Patch) has the potential to overcome these challenges resulting in better patient clinical outcomes and more cost-effective care.

Deep learning for automated sleep staging using instantaneous heart rate.

Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.

Erratum: Author Correction: Deep learning for automated sleep staging using instantaneous heart rate.

[This corrects the article DOI: 10.1038/s41746-020-0291-x.].

Burnout in the emergency department: Randomized controlled trial of an attention-based training program.

BACKGROUND: Burnout (encompassing emotional exhaustion, depersonalization and personal accomplishment) in healthcare professionals is a major issue worldwide. Emergency medicine physicians are particularly affected, potentially impacting on quality of care and attrition from the specialty. OBJECTIVE: The aim of this study was to apply an attention-based training (ABT) program to reduce burnout among emergency multidisciplinary team (MDT) members from a large urban hospital. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: Emergency MDT members were randomized to either a no-treatment control or an intervention group. Intervention group participants engaged in a four session (4 h/session) ABT program over 7 weeks with a practice target of 20 min twice-daily. Practice adherence was measured using a smart phone application together with a wearable Charge 2 device. MAIN OUTCOME MEASURES: The primary outcome was a change in burnout, comprising emotional exhaustion, depersonalization and personal achievement. The secondary outcomes were changes in other psychological and biometric parameters. RESULTS: The ABT program resulted in a significant reduction (P < 0.05; T1 [one week before intervention] vs T3 [follow-up at two months after intervention]) in burnout, specifically, emotional exhaustion, with an effect size (probability of superiority) of 59%. Similar reductions were observed for stress (P < 0.05) and anxiety (P < 0.05). Furthermore, ABT group participants demonstrated significant improvements in heart rate variability, resting heart rate, sleep as well as an increase in pro-inflammatory cytokine expression. CONCLUSION: This study describes a positive impact of ABT on emergency department staff burnout compared to a no-treatment control group. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT02887300.

The tuning of human motor response to risk in a dynamic environment task.

The role of motor uncertainty in discrete or static space tasks, such as pointing tasks, has been investigated in many experiments. These studies have shown that humans hold an internal representation of intrinsic and extrinsic motor uncertainty and compensate for this variability when planning movement. The aim of this study was to investigate how humans respond to uncertainties during movement execution in a dynamic environment despite indeterminate knowledge of the outcome of actions. Additionally, the role of errors, or lack thereof, in predicting risk was examined. In the experiment, subjects completed a driving simulation game on a two-lane road. The road contained random curves so that subjects were forced to use sensory feedback to complete the task and could not rely only on motor planning. Risk was manipulated by using horizontal perturbations to create the illusion of driving on a bumpy road, thereby imposing motor uncertainty, and altering the cost function of the road. Results suggest continual responsiveness to cost and uncertainty in a dynamic task and provide evidence that subjects avoid risk even in the absence of errors. The results suggest that humans tune their statistical motor behavior based on cost, taking into account probabilities of possible outcomes in response to environmental uncertainty.

Multi-input GNL-HybELS: an automated tool for the analysis of oculomotor dynamics during visual-vestibular interactions.

The eyes play a major role in our everyday activities. Eye movements are controlled by the oculomotor system, which enables us to stay focused on visual targets, switch visual attention, and compensate for external perturbations. This system's response to isolated visual or vestibular stimuli has been studied for decades, but what seems to be more critical is to know how it would respond to a combination of these stimuli, because in most natural situations, multiple stimuli are present. It is now believed that sensory fusion does not affect the dynamics of oculomotor modalities, despite studies suggesting otherwise. However, these interactions have not been studied in mathematical detail due to the lack of proper analysis tools and poor stimulus conditions. Here we propose an automated tool to analyze oculomotor responses without a-priori classification of nystagmus segments, where visual and vestibular stimuli are uncorrelated. Our method simultaneously classifies and identifies the responses of a multi-input multi-mode system. We validated our method on simulations, estimating sensory delays, semicircular canal time constant, and dynamics in both slow and fast phases of the response. Using this method, we can now investigate the effect of sensory fusion on the dynamics of oculomotor subsystems. With the analysis power of our new method, clinical protocols can now be improved to test these subsystems more efficiently and objectively.

GNL-HybELS: an algorithm to classify and identify VOR responses simultaneously.

The Vestibulo-Ocular Reflex (VOR) stabilizes the images of the world on the retinae when the head is in motion. Basic daily activities such as walking or driving depend on the proper functioning of this reflex. For several decades, scientists have developed methods to model and identify this system mathematically. However, traditional methods cannot analyze VOR data comprehensively because they disregard pieces of data (fast phases) which biases estimated reflex dynamics. Here we propose, for the first time, an automated tool to analyze entire VOR responses (slow and fast phases), without apriori classification of nystagmus segments.

Simultaneous identification of oculomotor subsystems using a hybrid system approach: introducing hybrid extended least squares.

The oculomotor system plays an essential role in our daily activities. It keeps the images of the world steady on the retina and enables us to track visual targets, or switch between targets. The modeling and identification of this system is key in the diagnosis and treatment of various diseases and lesions. Today, clinical protocols incorporate mathematical techniques to test the functionality of patients' oculomotor modalities through the analysis of the patients' responses to various stimuli. We have developed a new tool for simultaneous identification of the two modes of oculomotor responses, using hybrid extended least squares (HybELS), a novel identification method tailored for hybrid autoregressive moving average with exogenous input models. Previously, modified extended least squares (MELS) was proposed for the identification of vestibular nystagmus dynamics, one mode at a time. It involved searching for segment initial conditions (ICs) to avoid biased results. HybELS identifies both modes simultaneously, and does not require estimation of ICs. Results on experimental vestibuloocular reflex (VOR) data show that HybELS proves to be more robust than MELS with respect to identification of complex models. Furthermore, it is notably less computationally expensive than MELS. In the multi-input case, HybELS outperforms other tested methods, including MELS, both in parameter estimation and prediction error.

A Hybrid Extended Least Squares method (HybELS) for Vestibulo-Ocular Reflex identification.

The Vestibulo-Ocular Reflex (VOR) plays an essential role in the majority of daily activities by keeping the images of the world steady on the retina when either the environment or the body is moving. The modeling and identification of this system plays a key role in the diagnosis and treatment of various diseases and lesions, and their associated syndromes. Today, clinical protocols incorporate mathematical techniques for testing the functionality of patients' VORs through the analysis of the patients' responses to various stimuli. We have developed a new tool for simultaneous identification of the two modes of the horizontal VOR, using a novel algorithm. This algorithm, HybELS (Hybrid Extended Least Squares), is a regression-based identification method tailored for hybrid ARMAX (AutoRegressive Moving Average with eXogenous inputs) models, which can also be used for the identification of other neural systems. In the context of the VOR, MELS (Modified Extended Least Squares) has been proposed previously for the identification of vestibular nystagmus dynamics, one mode at a time. It also involved searching for segment initial conditions to avoid biased results. Our hybrid approach identifies the two modes simultaneously, and does not require estimation of initial conditions, since it takes advantage of state continuity in the transitions between fast and slow phases. The results on experimental VOR in the dark show that HybELS outperforms MELS in several aspects: It proves to be more robust than MELS with respect to the system order used for identification, while resulting in more accurate estimates in almost all contexts as well. Furthermore, due to the hybrid nature of the method, its calculations are algebraically more compact, and HybELS turns out to be much less computationally expensive than MELS.