Long-Term Safety and Efficacy of Transcatheter Microwave and Radiofrequency Denervation in a Chronic Ovine Model.

BACKGROUND: Transcatheter renal denervation (RDN) has had inconsistent efficacy and concerns for durability of denervation. We aimed to investigate long-term safety and efficacy of transcatheter microwave RDN in vivo in normotensive sheep in comparison to conventional radiofrequency ablation. METHODS AND RESULTS: Sheep underwent bilateral RDN, receiving 1 to 2 microwave ablations (maximum power of 80-120 W for 240 s-480 s) and 12 to 16 radiofrequency ablations (180 s-240 s) in the main renal artery in a paired fashion, alternating the side of treatment, euthanized at 2 weeks (acute N=15) or 5.5 months (chronic N=15), and compared with undenervated controls (N=4). Microwave RDN produced substantial circumferential perivascular injury compared with radiofrequency at both 2 weeks [area 239.8 (interquartile range [IQR] 152.0-343.4) mm2 versus 50.1 (IQR, 32.0-74.6) mm2, P <0.001; depth 16.4 (IQR, 13.9-18.9) mm versus 7.5 (IQR, 6.0-8.9) mm P <0.001] and 5.5 months [area 20.0 (IQR, 3.4-31.8) mm2 versus 5.0 (IQR, 1.4-7.3) mm2, P=0.025; depth 5.9 (IQR, 1.9-8.8) mm versus 3.1 (IQR, 1.2-4.1) mm, P=0.005] using mixed models. Renal denervation resulted in significant long-term reductions in viability of renal sympathetic nerves [58.9% reduction with microwave (P=0.01) and 45% reduction with radiofrequency (P=0.017)] and median cortical norepinephrine levels [71% reduction with microwave (P <0.001) and 72.9% reduction with radiofrequency (P <0.001)] at 5.5 months compared with undenervated controls. CONCLUSIONS: Transcatheter microwave RDN produces deep circumferential perivascular ablations without significant arterial injury to provide effective and durable RDN at 5.5 months compared with radiofrequency RDN.

Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review.

In clinical rhythmology, intracardiac bipolar electrograms (EGMs) play a critical role in investigating the triggers and substrates inducing and perpetuating atrial fibrillation (AF). However, the interpretation of bipolar EGMs is ambiguous due to several aspects of electrodes, mapping algorithms and wave propagation dynamics, so it requires several variables to describe the effects of these uncertainties on EGM analysis. In this narrative review, we critically evaluate the potential impact of such uncertainties on the design of cardiac mapping tools on AF-related substrate characterization. Literature suggest uncertainties are due to several variables, including the wave propagation vector, the wave's incidence angle, inter-electrode spacing, electrode size and shape, and tissue contact. The preprocessing of the EGM signals and mapping density will impact the electro-anatomical representation and the features extracted from the local electrical activities. The superposition of multiple waves further complicates EGM interpretation. The inclusion of these uncertainties is a nontrivial problem but their consideration will yield a better interpretation of the intra-atrial dynamics in local activation patterns. From a translational perspective, this review provides a concise but complete overview of the critical variables for developing more precise cardiac mapping tools.

Can Inter-Subject Associativity Predict Data-Driven BCI Performance?

Intra- and inter-subject variability causes covariate shifts in training and testing feature spaces, resulting in low sensorimotor (SMR) brain-computer interface (BCI) performance for practical implementation. Studies involving data-driven transfer learning strategies demonstrated improving BCI performance by covariate shift adaptation. In this study, we aim to illustrate if inter-subject associativity (e.g., subjects having similar SMR brain dynamics) can predict data-driven inter-subject BCI performance. We implemented a BCI classification pipeline with a common spatial pattern, principal component analysis and linear discriminant analysis for performance evaluation. Both intra- and inter-subject BCI were evaluated in 5-Fold Validation settings. We further proposed a Bhattacharyya distance-based covariate shift score (CSS) for assessing the difference between training and testing feature domains. We performed Pearson correlation analysis to draw the relation-ship between BCI performance and CSS. Intra-subject BCI performances were significantly and negatively correlated with CSS (r = -0.94, p < 0.05). For the inter-subject experiment, BCI performances were also highly and negatively associated with CSS (r = -0.61, p < 0.05). However, this data-driven BCI evaluation framework does not necessarily manifest inter-subject associativity in BCI performance, requiring further investigations for a conclusion.Clinical relevance- If it predicts BCI performance successfully, inter-subject associativity could reduce time-consuming and annoying subject-specific calibration for the users.

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset.

Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance- This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.

Enhanced Deep Transfer Learning Model based on Spatial-Temporal driven Scalograms for Precise Decoding of Motor Intent in Stroke Survivors.

Intelligent rehabilitation robotics (RR) have been proposed in recent years to aid post-stroke survivors recover their lost limb functions. However, a large proportion of these robotic systems operate in a passive mode that restricts users to predefined trajectories that rarely align with their intended limb movements, precluding full functional recovery. To address this issue, an efficient Transfer Learning based Convolutional Neural Network (TL-CNN) model is proposed to decode post-stroke patients' motion intentions toward realizing dexterously active robotic training during rehabilitation. For the first time, we use Spatial-Temporal Descriptor based Continuous Wavelet Transform (STD-CWT) as input to TL-CNN to optimally decode limb movement intent patterns. We evaluated the STD-CWT method on three distinct wavelets including the Morse, Amor, and Bump, and compared their decoding outcomes with those of the commonly adopted CWT technique under similar experimental conditions. We then validated the method using electromyogram signals of five stroke survivors who performed twenty-one distinct motor tasks. The results showed that the proposed technique recorded a significantly higher (p<0.05) decoding accuracy and faster convergence compared to the common method. Our method equally recorded obvious class separability for individual motor tasks across subjects. The findings suggest that the STD-CWT Scalograms have the potential for robust decoding of motor intention and could facilitate intuitive and active motor training in stroke RR.Clinical Relevance- The study demonstrated the potential of Spatial Temporal based Scalograms in aiding precise and robust decoding of multi-class motor tasks, upon which dexterously active rehabilitation robotic training for full motor function restoration could be realized.

An Attention-based Bidirectional LSTM Model for Continuous Cross-Subject Estimation of Knee Joint Angle during Running from sEMG Signals.

Accurate and robust estimation of joint kinematics via surface electromyogram (sEMG) signals provides a human-machine interaction (HMI)-based method that can be used to adequately control rehabilitation robots while performing complex movements, such as running, for motor function restoration in affected individuals. To this end, this paper proposes a deep learning-based model (AM-BiLSTM) that integrates a bidirectional long short-term memory (BiLSTM) network and an attention mechanism (AM) for robust estimation of joint kinematics. The proposed model was appraised using knee joint kinematic and sEMG signals collected from fourteen subjects who performed running at the speed of 2 m/s. The proposed model's generalizability was tested for both within- and cross-subject scenarios and compared with long short-term memory (LSTM) and multi-layer perceptron (MLP) networks in terms of normalized root-mean-square error and correlation coefficient metrics. Based on the statistical tests, the proposed AM-BiLSTM model significantly outperformed the LSTM and MLP methods in both within- and cross-subject scenarios (p<0.05) and achieved state-of-the-art performance.Clinical Relevance- The promising results of this study suggest that the AM-BiLSTM model has the potential for continuous cross-subject estimation of lower limb kinematics during running, which can be used to control sEMG-driven exoskeleton robots oriented towards rehabilitation training.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit.

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Fully 3D-Printed Dry EEG Electrodes.

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 Ω, with phase change of smaller than -30∘, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 Ω for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7-13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.

Ensemble Approach on Deep and Handcrafted Features for Neonatal Bowel Sound Detection.

For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis plan for early intervention. In this article, a novel technique is proposed for automated peristalsis sound detection from neonatal abdominal sound recordings and compared to various other machine learning approaches. It adopts an ensemble approach that utilises handcrafted as well as one and two dimensional deep features obtained from Mel Frequency Cepstral Coefficients (MFCCs). The results are then refined with the help of a hierarchical Hidden Semi-Markov Models (HSMM) strategy. We evaluate our method on abdominal sounds collected from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results of leave-one-patient-out cross validation show that our method provides an accuracy of 95.1% and an Area Under Curve (AUC) of 85.6%, outperforming both the baselines and the recent works significantly. These encouraging results show that our proposed Ensemble-based Deep Learning model is helpful for neonatologists to facilitate tele-health applications.

A validation and acceptability study of cognitive testing using switch and eye-gaze control technologies for children with motor and speech impairments: A protocol paper.

Despite the importance of knowing the cognitive capabilities of children with neurodevelopmental conditions, less than one-third of children with cerebral palsy participate in standardized assessments. Globally, approximately 50% of people with cerebral palsy have an intellectual disability and there is significant risk for domain-specific cognitive impairments for the majority of people with cerebral palsy. However, standardized cognitive assessment tools are not accessible to many children with cerebral palsy, as they require manual manipulation of objects, verbal response and/or speeded response. As such, standardised assessment may result in an underestimation of abilities for children with significant motor and/or speech impairment. The overall aim of the project is to examine and compare the psychometric properties of standardised cognitive assessment tools that have been accommodated for use with either a switch device or eye-gaze control technologies, with the specific aims to: (1) Examine the psychometric properties (measurement agreement and validity) of accommodated assessment tools by comparing the performance of typically developing children on six cognitive assessment tools administered via standardised versus accommodated (switch or eye-gaze control) administration; (2) Describe and compare the performance and user experience of children with cerebral palsy on six accommodated cognitive assessments administered via switch or eye-gaze control technologies. Secondary aims are to: (1) Describe the completion rates and time to complete assessments of participants in each group; (2) Within the group with cerebral palsy, examine the effects of condition-specific characteristics (type of cerebral palsy, functional levels, and pain) and demographics (age, socio-demographic) on participation. This protocol paper describes a two-phase validation and acceptability study that utilizes a mixed-model design. This study will collect concurrent data from 80 typically developing children and 40 children with cerebral palsy, who use switch or eye-gaze control technology as alternate access communication methods. The set of instruments will measure receptive vocabulary, fluid reasoning, sustained attention, vision perception, visuospatial working memory and executive functions. Data analyses will be conducted using SPSS v. 25 and R v 4.1.0. SPSS Sample Power 3 was used for power computation and allows for a 10% drop out rate. Quantitative descriptive statistics, measurement agreement data plotting, bivariate and multiple regressions analysis will be conducted using appropriate methods.

Timing errors and temporal uncertainty in clinical databases-A narrative review.

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field.

Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

An efficient and fast multi-band focused bioimpedance solution with EIT-based reconstruction for pulmonary embolism assessment: a simulation study from massive to segmental blockage.

Objective.Pulmonary embolism (PE) is an acute condition that blocks the perfusion to the lungs and is a common complication of Covid-19. However, PE is often not diagnosed in time, especially in the pandemic time due to complicated diagnosis protocol. In this study, a non-invasive, fast and efficient bioimpedance method with the EIT-based reconstruction approach is proposed to assess the lung perfusion reliably.Approach.Some proposals are presented to improve the sensitivity and accuracy for the bioimpedance method: (1) a new electrode configuration and focused pattern to help study deep changes caused by PE within each lung field separately, (2) a measurement strategy to compensate the effect of different boundary shapes and varied respiratory conditions on the perfusion signals and (3) an estimator to predict the lung perfusion capacity, from which the severity of PE can be assessed. The proposals were tested on the first-time simulation of PE events at different locations and degrees from segmental blockages to massive blockages. Different object boundary shapes and varied respiratory conditions were included in the simulation to represent for different populations in real measurements.Results.The correlation between the estimator and the perfusion was very promising (R = 0.91, errors <6%). The measurement strategy with the proposed configuration and pattern has helped stabilize the estimator to non-perfusion factors such as the boundary shapes and varied respiration conditions (3%-5% errors).Significance.This promising preliminary result has demonstrated the proposed bioimpedance method's capability and feasibility, and might start a new direction for this application.

Four-channel current switching device to enable multi-electrode magnetic resonance current density imaging.

Neurostimulation with multiple scalp electrodes has shown enhanced effects in recent studies. However, visualizations of stimulation-induced internal current distributions in brain is only possible through simulated current distributions obtained from computer model of human head. While magnetic resonance current density imaging (MRCDI) has a potential for direct in-vivo measurement of currents induced in brain with multi-electrode stimulation, existing MRCDI methods are only developed for two-electrode neurostimulation. A major bottleneck is the lack of a current switching device which is typically used to convert the DC current of neurostimulation devices into user-defined waveforms of positive and negative polarity with delays between them. In this work, we present a design of a four-electrode current switching device to enable simultaneous switching of current flowing through multiple scalp electrodes.

Seizure Susceptibility Prediction in Uncontrolled Epilepsy.

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to several potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a challenging issue of balancing sensitivity and false alarms. There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker for seizure prediction?" are yet to be fully answered. In this paper, we introduce a tool to facilitate the exploration of the potential biomarkers. We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.

Optimizing Impedance Change Measurement During Radiofrequency Ablation Enables More Accurate Characterization of Lesion Formation.

OBJECTIVES: This study sought to determine whether a novel impedance thermal imaging system (ITIS) provides an impedance measurement that is better correlated with lesion dimensions than circuit impedance during radiofrequency (RF) ablation. BACKGROUND: A 5- to 10-Ω impedance drop is clinically used to corroborate an effective RF ablation lesion. However, the contribution of local tissue heating to circuit impedance change is small and dependent on the local environment of the catheter and placement of the grounding patch. METHODS: ITIS uses ablation catheter and skin electrodes to perform 4-terminal impedance measurements with separate voltage sensing and current injection electrode pairs. Seven sheep underwent endocardial ventricular irrigated RF ablation at 40 W for 60 s. ITIS impedance and circuit impedance were both measured throughout ablation. When the sheep were sacrificed, ablation lesions were cut along their long axis; the depth, width, and surface area of the cut surface were measured. RESULTS: A total of 68 RF ablations were performed, with a median depth of 3.5 mm (interquartile range [IQR]: 2.1 to 4.9 mm), width of 8.3 mm (IQR: 5.7 to 10.8 mm), and surface area of 23.8 mm2 (IQR: 9.3 to 43.0 mm2). ITIS impedance change had good correlation with lesion depth, width, and surface area (R = 0.76, R = 0.87, and R = 0.87, respectively); and superior to circuit impedance for lesion depth, width, and surface area (p = 0.0018, p = 0.0004, and p = 0.0001, respectively). CONCLUSIONS: By optimizing the current path and using 4-terminal impedance measurement during RF ablation, the contribution of tissue temperature changes to measured impedance is better standardized to provide a more reliable measure than conventional ablation circuit impedance.

Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3-24 Months.

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3-24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3-24 months (all subjects), 0-6 months, and 7-24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.

Blood Pressure in Critically Ill Children: Exploratory Analyses of Concurrent Invasive and Noninvasive Measurements.

OBJECTIVES: Differences and biases between directly measured intra-arterial blood pressure and intermittingly measured noninvasive blood pressure using an oscillometric cuff method have been reported in adults and children. At the bedside, clinicians are required to assign a confidence to a specific blood pressure measurement before acting upon it, and this is challenging when there is discordance between measurement techniques. We hypothesized that big data could define and quantify the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements and how they can be influenced by patient characteristics, thereby aiding bedside decision-making. DESIGN: A retrospective analysis of cuff blood pressure readings with associated concurrent invasive arterial blood pressure measurements (452,195 noninvasive blood pressure measurements). SETTING: Critical care unit at The Hospital for Sick Children, Toronto. PATIENTS: Six-thousand two-hundred ninety-seven patients less than or equal to 18 years old, hospitalized in a critical care unit with an indwelling arterial line. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two-dimensional distributions of intra-arterial blood pressure and noninvasive blood pressure were generated and the conditional distributions of intra-arterial blood pressure examined as a function of the noninvasive systolic, diastolic, or mean blood pressure. Modification of these distributions according to age and gender were examined using a multilevel mixed-effects model. For any given combination of patient age and noninvasive blood pressure, the expected distribution of intra-arterial blood pressure readings exhibited marked variability at the population level and a bias that significantly depended on the noninvasive blood pressure value and age. We developed an online tool that allows exploration of the relationship between noninvasive blood pressure and intra-arterial blood pressure and the conditional probability distributions according to age. CONCLUSIONS: A large physiologic dataset provides clinically applicable insights into the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements that can help guide decision-making at the patient bedside.

Derivation of Respiratory Metrics in Health and Asthma.

The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. METHODS: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. RESULTS: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all p < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(-2.31, 2.34) vs. -0.02(-2.19, 2.16), expiration time -0.19(-2.35, 1.98) vs. -0.24(-2.36, 1.89), and inter-breath intervals -0.19(-2.73, 2.35) vs. -0.25(2.76, 2.26). The inspiratory:expiratory ratios were -0.14(-1.43, 1.16) vs. -0.14(-1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(-2.51, 2.96) vs. 0.29(-2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). CONCLUSION: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.