Construction of a comprehensive fetal monitoring database for the study of perinatal hypoxic ischemic encephalopathy.

This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth. (2) Assigning each birth to an objectively defined outcome class that included normal, acidosis, and hypoxic-ischemic encephalopathy. (3) Removing all personal health information from the EFM recordings and clinical records. (4) Preprocessing the deidentified EFM records to eliminate duplicates, reformat the signals, combine signals from different sensors, and bridge gaps to generate signals in a format that can be readily analyzed. (5) Post-processing the repaired EFM recordings to extract key features of the fetal heart rate, uterine activity, and their relations. (6) Populating a database that links the clinical information, EFM records, and EFM features to support easy querying and retrieval. •A multi-step process is required to build a comprehensive database linking electronic temporal fetal monitoring signals to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome.•The current database documents more than 250,000 births including almost 4,000 acidosis and 400 HIE cases. This represents more than 80% of the births that occurred in 15 Northern California Kaiser Permanente Hospitals between 2011-2019. This is a valuable resource for studying the factors predictive of outcome.•The signal processing code and schemas for the database are freely available. The database will not be permitted to leave Kaiser firewalls, but a process is in place to allow interested investigators to access it.

Comparison of Wired and Wireless Heart Rate Monitoring in the Neonatal Intensive Care Unit.

In the Neonatal Intensive Care Unit (NICU), infants' vital signs are monitored on a continuous basis via wired devices. These often interfere with patient care and pose increased risks of skin damage, infection, and tangling around the body. Recently, a wireless system for neonatal monitoring called ANNEⓇ One (Sibel Health, Chicago, USA) was developed. We designed an ongoing study to evaluate the feasibility, reliability and accuracy, of using this system in the NICU. Vital signals were simultaneously acquired by using the standard, wired clinical monitor and the ANNEⓇ device. Data from 10 NICU infants were recorded for 8 hours per day during 4 consecutive days. Initial analysis of the heart rate (HR) data revealed four problems in comparing the signals: 1) gaps in the signals - periods of time for which data were unavailable, 2) wired and wireless signals were sampled at different rates, 3) a delay between the sampled values of wired and wireless signals, and 4) this delay increased with time. To address these problems, we developed a pre-processing algorithm that interpolated samples in short gaps, resampled the signals to an equal rate, estimated the delay and drift rate between corresponding signals, and aligned the signals. Applications of the pre-processing algorithm to 40 recordings demonstrated that it was very effective. A strong agreement between wireless and wired HR signals was seen, with an average correlation of 0.95±0.04, a slope of 1.00, and a variance accounted for 89.56±7.62%. Bland-Altman analysis showed a low bias across the ensemble, with an average difference of 0.11 (95% confidence interval of -0.02 to 0.24) bpm.Clinical relevance- This algorithm provides the means for a detailed comparison of wired and wireless monitors in the NICU.

Timely Detection of Infants at Risk of Intrapartum Acidosis and Hypoxic-Ischemic Encephalopathy Using Cardiotocography.

This work aims to improve the intrapartum detection of fetuses with an increased risk of developing fetal acidosis or hypoxic-ischemic encephalopathy (HIE) using fetal heart rate (FHR) and uterine pressure (UP) signals. Our study population comprised 40,831 term births divided into 3 classes based on umbilical cord or early neonatal blood gas assessments: 374 with verified HIE, 3,047 with acidosis but no encephalopathy and 37,410 healthy babies with normal gases. We developed an intervention recommendation system based on a random forest classifier. The classifier was trained using classical and novel features extracted electronically from 20-minute epochs of FHR and UP. Then, using the predictions of the classifier on each epoch, we designed a decision rule to determine when to recommended intervention. Compared to the Caesarean rates in each study group, our system identified an additional 5.68% of babies who developed HIE (54.55% vs 60.23%, p < 0.01) with a specific alert threshold. Importantly, about 75% of these recommendations were made more than 200 minutes before birth. In the acidosis group, the system identified an additional 17.44% (37.15% vs 54.59%, p < 0.01) and about 2/3 of these recommendations were made more than 200 minutes before birth. Compared to the Caesarean rate in the healthy group, the associated false positive rate was increased by 1.07% (38.80% vs 39.87%, p<0.01).Clinical Relevance- This method recommended intervention in more babies affected by acidosis or HIE, than the intervention rate observed in practice and most often did so 200 minutes before delivery. This was early enough to expect that interventions would have clinical benefit and reduce the rate of HIE. Given the high burden associated with HIE, this would justify the marginal increase in the normal Cesarean rate.

Accounting for Nulliparity in the Prediction of Hypoxic-Ischemic Encephalopathy Using Cardiotocography.

Nulliparous pregnancies, those where the mother has not previously given birth, are associated with longer labors and hence expose the fetus to more contractions and other adverse intrapartum conditions such as chorioamnionitis. The objective of the present study was to test if accounting for nulliparity could improve the detection of fetuses at increased risk of developing hypoxic-ischemic encephalopathy (HIE). During labor, clinicians assess the fetal heart rate and uterine pressure signals to identify fetuses at risk of developing HIE. In this study, we performed random forest classification using fetal heart rate and uterine pressure features from 40,831 births, including 374 that developed HIE. We analyzed a two-path classification approach that analyzed separately the fetuses from nulliparous and multiparous mothers, and a one-path classification approach that included the clinical variable for nulliparity as a classification feature. We compared these two approaches to a one-path classifier that had no information about the parity of the mothers. We also compared our results to the rate of Caesarean deliveries in each group, which is used clinically to interrupt the progression towards HIE. All the classifiers detected more fetuses that developed HIE than the observed Caesarean rate, but accounting for nulliparity did not improve performance.

The use of wireless sensors in the neonatal intensive care unit: a study protocol.

Background: Continuous monitoring of vital signs and other biological signals in the Neonatal Intensive Care Unit (NICU) requires sensors connected to the bedside monitors by wires and cables. This monitoring system presents challenges such as risks for skin damage or infection, possibility of tangling around the patient body, or damage of the wires, which may complicate routine care. Furthermore, the presence of cables and wires can act as a barrier for parent-infant interactions and skin to skin contact. This study will investigate the use of a new wireless sensor for routine vital monitoring in the NICU. Methods: Forty-eight neonates will be recruited from the Montreal Children's Hospital NICU. The primary outcome is to evaluate the feasibility, safety, and accuracy of a wireless monitoring technology called ANNE® One (Sibel Health, Niles, MI, USA). The study will be conducted in 2 phases where physiological signals will be acquired from the standard monitoring system and the new wireless monitoring system simultaneously. In phase 1, participants will be monitored for 8 h, on four consecutive days, and the following signals will be obtained: heart rate, respiratory rate, oxygen saturation and skin temperature. In phase 2, the same signals will be recorded, but for a period of 96 consecutive hours. Safety and feasibility of the wireless devices will be assessed. Analyses of device accuracy and performance will be accomplished offline by the biomedical engineering team. Conclusion: This study will evaluate feasibility, safety, and accuracy of a new wireless monitoring technology in neonates treated in the NICU.

Clinical usefulness of reintubation criteria in extremely preterm infants: a cohort study.

OBJECTIVE: To describe the thresholds of instability used by clinicians at reintubation and evaluate the accuracy of different combinations of criteria in predicting reintubation decisions. DESIGN: Secondary analysis using data obtained from the prospective observational Automated Prediction of Extubation Readiness study (NCT01909947) between 2013 and 2018. SETTING: Multicentre (three neonatal intensive care units). PATIENTS: Infants with birth weight ≤1250 g, mechanically ventilated and undergoing their first planned extubation were included. INTERVENTIONS: After extubation, hourly O2 requirements, blood gas values and occurrence of cardiorespiratory events requiring intervention were recorded for 14 days or until reintubation, whichever came first. MAIN OUTCOME MEASURES: Thresholds at reintubation were described and grouped into four categories: increased O2, respiratory acidosis, frequent cardiorespiratory events and severe cardiorespiratory events (requiring positive pressure ventilation). An automated algorithm was used to generate multiple combinations of criteria from the four categories and compute their accuracies in capturing reintubated infants (sensitivity) without including non-reintubated infants (specificity). RESULTS: 55 infants were reintubated (median gestational age 25.2 weeks (IQR 24.5-26.1 weeks), birth weight 750 g (IQR 640-880 g)), with highly variable thresholds at reintubation. After extubation, reintubated infants had significantly greater O2 needs, lower pH, higher pCO2 and more frequent and severe cardiorespiratory events compared with non-reintubated infants. After evaluating 123 374 combinations of reintubation criteria, Youden indices ranged from 0 to 0.46, suggesting low accuracy. This was primarily attributable to the poor agreement between clinicians on the number of cardiorespiratory events at which to reintubate. CONCLUSIONS: Criteria used for reintubation in clinical practice are highly variable, with no combination accurately predicting the decision to reintubate.

Wireless monitoring devices in hospitalized children: a scoping review.

The purpose of this study is to provide a structured overview of existing wireless monitoring technologies for hospitalized children. A systematic search of the literature published after 2010 was conducted in Medline, Embase, Scielo, Cochrane, and Web of Science. Two investigators independently reviewed articles to determine eligibility for inclusion. Information on study type, hospital setting, number of participants, use of a reference sensor, type and number of vital signs monitored, duration of monitoring, type of wireless information transfer, and outcomes of the wireless devices was extracted. A descriptive analysis was applied. Of the 1130 studies identified from our search, 42 met eligibility for subsequent analysis. Most included studies were observational studies with sample sizes of 50 or less published between 2019 and 2022. Common problems pertaining to study methodology and outcomes observed were short duration of monitoring, single focus on validity, and lack information on wireless transfer and data management.  Conclusion: Research on the use of wireless monitoring for children in hospitals has been increasing in recent years but often limited by methodological problems. More rigorous studies are necessary to establish the safety and accuracy of novel wireless monitoring devices in hospitalized children. What is Known: • Continuous monitoring of vital signs using wired sensors is the standard of care for hospitalized pediatric patients. However, the use of wires may pose significant challenges to optimal care. What is New: • Interest in wireless monitoring for hospitalized pediatric patients has been rapidly growing in recent years. • However, most devices are in early stages of clinical testing and are limited by inconsistent clinical and technological reporting.

Non-Parametric Nonlinear Parameter-Varying Parallel-Cascade Identification of Dynamic Joint Stiffness.

OBJECTIVE: The paper presents a method to identify ankle joint dynamic stiffness during functional tasks where intrinsic and reflex stiffness change with a time-varying scheduling variable (SV), such as joint position or torque. METHODS: The method models joint stiffness with two pathways: (1) A parameter-varying (PV) impulse response function (IRF) describing intrinsic stiffness; and (2) a reflex stiffness model comprising a PV static nonlinearity followed by a PV linear element. RESULTS: Monte-Carlo simulations demonstrated that the method accurately estimated all elements of the intrinsic and reflex pathways as they changed with a SV. Experimental results with a healthy individual subjected to large, imposed ankle movements demonstrated that: (a) Intrinsic stiffness changed substantially as a function of ankle position; elasticity was lowest near the mid-position and increased with either dorsiflexion or plantarflexion. (b) Reflex gain increased and the velocity threshold for reflex excitation decreased monotonically with ankle dorsiflexion. (c) Reflex dynamics resembled a second-order, low-pass system that was invariant with ankle position. (d) The identified PV Parallel-Cascade (PC) model accurately predicted the torque response to novel trajectories of ankle movement. CONCLUSION: The PV-PC method can accurately and reliably estimate how intrinsic and reflex stiffness change with a time-varying SV. SIGNIFICANCE: The method is novel with multiple advantages: (a) It provides a unified algorithm that characterizes the changes in the parameters of all joint stiffness elements needed to understand their role in postural/movement control; (b) It is efficient requiring only two trials; (c) The models identified can predict the joint stiffness response to novel movements informing orthoses and prostheses design.

Age at First Extubation Attempt and Death or Respiratory Morbidities in Extremely Preterm Infants.

OBJECTIVE: To describe the timing of first extubation in extremely preterm infants and explore the relationship between age at first extubation, extubation outcome, and death or respiratory morbidities. STUDY DESIGN: In this subanalysis of a multicenter observational study, infants with birth weights of 1250 g or less and intubated within 24 hours of birth were included. After describing the timing of first extubation, age at extubation was divided into early (within 7 days from birth) vs late (days of life 8-35), and extubation outcome was divided into success vs failure (reintubation within 7 days after extubation), to create 4 extubation groups: early success, early failure, late success, and late failure. Logistic regression analyses were performed to evaluate associations between the 4 groups and death or bronchopulmonary dysplasia, bronchopulmonary dysplasia among survivors, and durations of respiratory support and oxygen therapy. RESULTS: Of the 250 infants included, 129 (52%) were extubated within 7 days, 93 (37%) between 8 and 35 days, and 28 (11%) beyond 35 days of life. There were 93, 36, 59, and 34 infants with early success, early failure, late success, and late failure, respectively. Although early success was associated with the lowest rates of respiratory morbidities, early failure was not associated with significantly different respiratory outcomes compared with late success or late failure in unadjusted and adjusted analyses. CONCLUSIONS: In a contemporary cohort of extremely preterm infants, early extubation occurred in 52% of infants, and only early and successful extubation was associated with decreased respiratory morbidities. Predictors capable of promptly identifying infants with a high likelihood of early extubation success or failure are needed.

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study.

BACKGROUND: Extremely preterm infants are frequently subjected to mechanical ventilation. Current prediction tools of extubation success lacks accuracy. METHODS: Multicenter study including infants with birth weight ≤1250 g undergoing their first extubation attempt. Clinical data and cardiorespiratory signals were acquired before extubation. Primary outcome was prediction of extubation success. Automated analysis of cardiorespiratory signals, development of clinical and cardiorespiratory features, and a 2-stage Clinical Decision-Balanced Random Forest classifier were used. A leave-one-out cross-validation was done. Performance was analyzed by ROC curves and determined by balanced accuracy. An exploratory analysis was performed for extubations before 7 days of age. RESULTS: A total of 241 infants were included and 44 failed (18%) extubation. The classifier had a balanced accuracy of 73% (sensitivity 70% [95% CI: 63%, 76%], specificity 75% [95% CI: 62%, 88%]). As an additional clinical-decision tool, the classifier would have led to an increase in extubation success from 82% to 93% but misclassified 60 infants who would have been successfully extubated. In infants extubated before 7 days of age, the classifier identified 16/18 failures (specificity 89%) and 73/105 infants with success (sensitivity 70%). CONCLUSIONS: Machine learning algorithms may improve a balanced prediction of extubation outcomes, but further refinement and validation is required. IMPACT: A machine learning-derived predictive model combining clinical data with automated analyses of individual cardiorespiratory signals may improve the prediction of successful extubation and identify infants at higher risk of failure with a good balanced accuracy. Such multidisciplinary approach including medicine, biomedical engineering and computer science is a step forward as current tools investigated to predict extubation outcomes lack sufficient balanced accuracy to justify their use in future trials or clinical practice. Thus, this individualized assessment can optimize patient selection for future trials of extubation readiness by decreasing exposure of low-risk infants to interventions and maximize the benefits of those at high risk.

Multi-Chain Semi-Markov Analysis of Intrapartum Cardiotocography.

Visual assessment of the evolution of fetal heart rate (FHR) and uterine pressure (UP) patterns is the standard of care in the intrapartum period. Unfortunately, this assessment has high levels of intra- and inter-observer variability. This study processed and analyzed FHR and UP patterns using computerized pattern recognition tools. The goal was to evaluate differences in FHR and UP patterns between fetuses with normal outcomes and those who developed hypoxic-ischemic encephalopathy (HIE). For this purpose, we modeled the sequence of FHR patterns and uterine contractions using Multi-Chain Semi-Markov models (MCSMMs). These models estimate the probability of transitioning between FHR or UP patterns and the dwell time of each pattern. Our results showed that in comparison to the control group, the HIE group had: (1) more frequent uterine contractions during the last 12 hours before birth; (2) more frequent FHR decelerations during the last 12 hours before birth; (3) longer decelerations during the last eight hours before birth; and (4) shorter baseline durations during the last five hours before birth. These results demonstrate that the fetuses in the HIE group were subject to a more stressful environment than those in the normal group. Clinical Relevance- Our results revealed statistically significant differences in FHR/UP patterns between the normal and HIE groups in the hours before birth. This indicates that features derived using MCSMMs may be useful in a machine learning framework to detect infants at increased risk of developing HIE allowing preventive interventions.

Identification of human balance control responses to visual inputs using virtual reality.

Human upright balance is maintained through feedback mechanisms that use a variety of sensory modalities. Vision senses information about the position and velocity of the visual surround motion to improve balance by reducing the sway evoked by external disturbances. This study characterized the effects of visual information on human anterior-posterior body sway in upright stance by presenting perturbations through a virtual reality system. This made it possible to use a new visual perturbation signal, based on trapezoidal velocity pulses, whose amplitude and velocity could be controlled separately. To date, the influences of visual field position and velocity have only been studied independently due to the experimental limitations. The hip displacement, ankle torques, shank angles, and surface EMGs of four major ankle muscles were measured bilaterally as outputs. We found that the root mean square (RMS) hip displacement (body angle) increased systematically with visual input amplitude. However, for each amplitude, the RMS body angle increased when input velocity was changed from 2 to 5 degrees per second (dps) and then decreased from 5 to 10 dps. Spectral analysis was used to compute frequency response over a frequency range from 0.04 to 0.6 Hz. The gain of body sway relative to the perturbation increased with frequency, whereas the coherence declined. Moreover, as the stimulus amplitude increased, the gain generally decreased, whereas the mean coherence values always increased. The mean gains and mean coherence values were greatest for the velocity of 5 dps. This study presents a novel experimental approach to study human postural control and augments our knowledge of how visual information is processed in the central nervous system to maintain balance.NEW & NOTEWORTHY In this paper, we developed a new methodological approach to study the effects of visual information on dynamic body sway. We used VR to apply visual perturbations to induce AP body sway. We designed a new visual stimulus waveform based on trapezoidal velocity pulses whose peak-to-peak amplitude and velocity could be modulated independently. Subsequently, we investigated how the amplitude and velocity of visual field motion influence the postural responses evoked in healthy adults.

The Nonlinear Dynamic Response of Intrapartum Fetal Heart Rate to Uterine Pressure.

The research objective of our group is to improve the intrapartum detection of cardiotocography tracings associated with an increased risk of developing fetal acidosis and subsequent hypoxic-ischemic encephalopathy (HIE). The detection methods that we aim to develop must be sensitive to abnormal tracings without causing excessive unnecessary interventions. Past studies showed that the dynamic response of fetal heart rate (FHR) to uterine pressure (UP) during the intrapartum could be modelled using linear systems. In this study, we examined the assumption of linearity by comparing the performance of linear dynamic and nonlinear dynamic models of the UP-FHR system. The linear systems were defined by second-order state-space models. The nonlinear systems were defined by Hammerstein models: a cascade of a static nonlinearity and a linear second-order state-space model. Our results showed that nonlinear dynamic models were better than linear systems in 81.8% of UP-FHR segments.

Estimation and Discriminability of Doppler Ultrasound Fetal Heart Rate Variability Measures.

Continuous electronic fetal monitoring and the access to databases of fetal heart rate (FHR) data have sparked the application of machine learning classifiers to identify fetal pathologies. However, most fetal heart rate data are acquired using Doppler ultrasound (DUS). DUS signals use autocorrelation (AC) to estimate the average heartbeat period within a window. In consequence, DUS FHR signals loses high frequency information to an extent that depends on the length of the AC window. We examined the effect of this on the estimation bias and discriminability of frequency domain features: low frequency power (LF: 0.03-0.15 Hz), movement frequency power (MF: 0.15-0.5 Hz), high frequency power (HF: 0.5-1 Hz), the LF/(MF + HF) ratio, and the nonlinear approximate entropy (ApEn) as a function of AC window length and signal to noise ratio. We found that the average discriminability loss across all evaluated AC window lengths and SNRs was 10.99% for LF 14.23% for MF, 13.33% for the HF, 10.39% for the LF/(MF + HF) ratio, and 24.17% for ApEn. This indicates that the frequency domain features are more robust to the AC method and additive noise than the ApEn. This is likely because additive noise increases the irregularity of the signals, which results in an overestimation of ApEn. In conclusion, our study found that the LF features are the most robust to the effects of the AC method and noise. Future studies should investigate the effect of other variables such as signal drop, gestational age, and the length of the analysis window on the estimation of fHRV features and their discriminability.

Identification of Central and Stretch Reflex Contributions to Human Postural Control.

Human postural control requires continuous modulation of ankle torque to stabilize the upright stance. The torque is generated by two components: active contributions, due to central control and stretch reflex, and passive mechanisms, due to joint intrinsic stiffness. Identifying the contribution of each component is difficult, since their effects appear together, and standing is controlled in closed-loop. This article presents a novel multiple-input, single-output method to identify central and stretch reflex contributions to human postural control. The model uses ankle muscle EMGs as inputs and requires no kinematic data. Application of the method to data from nine subjects during standing while subjected to perturbations of ankle position demonstrated that active torque accounted for 84.0± 5.5% of the ankle torque. The ankle plantar-flexors collectively produced the largest portion of the active torque through central control, with large inter-subject variability in the relative contributions of the individual muscles. In addition, reflex contribution of the plantar-flexors was substantial in half of the subjects, showing its potentially important functional role; finally, intrinsic contributions, estimated as the residual of the model, contributed to 15% of the torque. This study introduces a new method to quantify the contributions of the central and stretch reflex pathways to postural control; the method also provides an estimate of noisy intrinsic torque with significantly increased signal to noise ratio, suitable for identification of intrinsic stiffness in standing. The method can be used in different experimental conditions and requires minimal a-priori assumption regarding the role of different pathways in postural control.

A Model-Based Insulin Dose Optimization Algorithm for People With Type 1 Diabetes on Multiple Daily Injections Therapy.

OBJECTIVE: Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D) including basal insulin doses to keep glucose levels constant during fasting conditions and bolus insulin doses with meals. Optimal insulin dosing is critical to achieving satisfactory glycemia but is challenging due to inter- and intra-individual variability. Here, we present a novel model-based iterative algorithm that optimizes insulin doses using previous-day glucose, insulin, and meal data. METHODS: Our algorithm employs a maximum-a-posteriori method to estimate parameters of a model describing the effects of changes in basal-bolus insulin doses. Then, parameter estimates, their confidence intervals, and the goodness of fit, are combined to generate new recommendations. We assessed our algorithm in three ways. First, a clinical data set of 150 days (15 participants) were used to evaluate the proposed model and the estimation method. Second, 60-day simulations were performed to demonstrate the efficacy of the algorithm. Third, a sample 6-day clinical experiment is presented and discussed. RESULTS: The model fitted the clinical data well with a root-mean-square-error of 1.75 mmol/L. Simulation results showed an improvement in the time in target (3.9-10 mmol/L) from 64% to 77% and a decrease in the time in hypoglycemia (< 3.9 mmol/L) from 8.1% to 3.8%. The clinical experiment demonstrated the feasibility of the algorithm. CONCLUSION: Our algorithm has the potential to improve glycemic control in people with T1D using MDI. SIGNIFICANCE: This work is a step forward towards a decision support system that improves their quality of life.

Temporal Evolution of Intrapartum Fetal Heart Rate Features.

Our research goal is to improve the intrapartum identification of tracings associated with severe acidosis at birth and subsequent hypoxic-ischemic encephalopathy so that timely interventions could avoid such complications without causing excessive unnecessary interventions in births with normal outcomes. The present study examines the evolution of fetal heart rate (FHR) features over the course of labor. We analyzed FHR signals collected in the last 6 hours before delivery in 21,853 births with normal neonatal outcomes and in 163 that developed hypoxic-ischemic encephalopathy (HIE) from 15 hospitals of Kaiser Permanente Northern California. We divided these six hours into 18 nonoverlapping 20-minute epochs. The power spectral density of each epoch was divided into three bands: low frequency (LF, 30-150 mHz), movement frequency (MF, 150-500 mHz), and high frequency (HF, 500-1000 mHz). We also estimated the LF/(MF+HF) ratio, the mean and standard deviation of the FHR signal, the approximate entropy (ApEn), and the deceleration capacity (DC). In our results, ApEn, the standard deviation, and DC showed a promising ability to detect risk of HIE as early as 120 minutes before birth, which gives enough leading time for timely interventions.

Contributions of Vision in Human Postural Control: A Virtual Reality-based Study.

During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system. Moreover, we designed a new perturbation signal based on trapezoidal velocity (TrapV) pulses enabling us to simultaneously examine the effects of amplitude and velocity on balance control. The experiments included four different peak-to-peak amplitudes (1-10 degrees), and three velocities (2-10 degree/sec). The body angle, ankle torques and shank angles were measured and analyzed in response to each perturbation. The results reveal that stimuli with higher amplitudes evoked larger responses, while they were initially increased and reached a peak, then decreased by increasing the motion velocity of visual surround.

Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals.

Infants are at risk for potentially life-threatening postoperative apnea (POA). We developed an Automated Unsupervised Respiratory Event Analysis (AUREA) to classify breathing patterns obtained with dual belt respiratory inductance plethysmography and a reference using Expectation Maximization (EM). This work describes AUREA and evaluates its performance. AUREA computes six metrics and inputs them into a series of four binary k-means classifiers. Breathing patterns were characterized by normalized variance, nonperiodic power, instantaneous frequency and phase. Signals were classified sample by sample into one of 5 patterns: pause (PAU), movement (MVT), synchronous (SYB) and asynchronous (ASB) breathing, and unknown (UNK). MVT and UNK were combined as UNKNOWN. Twenty-one preprocessed records obtained from infants at risk for POA were analyzed. Performance was evaluated with a confusion matrix, overall accuracy, and pattern specific precision, recall, and F-score. Segments of identical patterns were evaluated for fragmentation and pattern matching with the EM reference. PAU exhibited very low normalized variance. MVT had high normalized nonperiodic power and low frequency. SYB and ASB had a median frequency of respectively, 0.76Hz and 0.71Hz, and a mode for phase of 4o and 100o. Overall accuracy was 0.80. AUREA confused patterns most often with UNKNOWN (25.5%). The pattern specific F-score was highest for SYB (0.88) and lowest for PAU (0.60). PAU had high precision (0.78) and low recall (0.49). Fragmentation was evident in pattern events <2s. In 75% of the EM pattern events >2s, 50% of the samples classified by AUREA had identical patterns. Frequency and phase for SYB and ASB were consistent with published values for synchronous and asynchronous breathing in infants. The low normalized variance in PAU, was consistent with published scoring rules for pediatric apnea. These findings support the use of AUREA to classify breathing patterns and warrant a future evaluation of clinically relevant respiratory events.

Postprandial hyperglycaemia following insulin suspensions by the artificial pancreas: Implications for bolus calculators.

Conventional bolus calculators apply negative prandial corrections when premeal glucose levels are low. However, no study has evaluated the need for this negative correction with closed-loop systems. We analysed data retrospectively from a cohort study evaluating a closed-loop artificial pancreas system conducted in a diabetes camp over a period of 11 days. Meal boluses with negative correction (n = 98) of 47 participants aged 8 to 22 years were examined. If there was no insulin-on-board from previous boluses at mealtime, the postprandial hyperglycaemia rate increased with increased duration of insulin suspension (P = .03), with odds ratios being exaggerated by 17% per 10 minutes of suspension. However, if there was insulin-on-board from previous boluses, the hyperglycaemia rate did not change with increased duration of insulin suspension (P = .24). When there was no insulin-on-board, the rate of hyperglycaemia after meals preceded by no suspension was 21% (3/14), compared with 52% (12/23) and 64% (9/14) after meals preceded by suspensions of ≥50 and ≥70 minutes, respectively. Meal size did not influence these results. We conclude that, in the absence of insulin-on-board, negative prandial corrections may not be necessary following long insulin suspensions.