Evaluating inter-individual variability captured by the Eleveld pharmacokinetics model

Inter-individual variability in Pharmacokinetic (PK) and Pharmacodynamic (PD) models significantly affects the accuracy of Target Controlled Infusion and closed-loop control of anesthesia. We hypothesize that the novel Eleveld PK model captures more inter-individual variability relevant to both open-loop and closed-loop control design, resulting in reduced variability in PD models identified using the Eleveld PK model's plasma prediction compared to the Schuttler or Schnider PK model. We used a dataset of propofol infusion rates and Depth of Hypnosis measurements across three demographic groups: elderly, obese, and adult. PD models are identified based on plasma concentration prediction using three PK models (Schuttler, Schnider, and Eleveld). Validation methods are presented to confirm acceptable predictive performance and comparable PK-PD model variability within each demographic group. To test our hypothesis, we compared coefficient variations in step responses for open-loop control and multiplicative uncertainty of PD model sets for closed-loop control. Validated PKPD models using the Schuttler and Schnider PK model showed no significant differences in predictive response and multiplicative uncertainty compared to the Eleveld PK model. The coefficient variations in step responses of PD model sets and the frequency ranges, corresponding to uncertainty below one, were comparable for all three PK models. The comparison of the accumulated coefficient of variation in the step-response and the uncertainty of the PD model sets indicated that the Eleveld PK model does not offer any advantage for the design of open-loop or closed-loop control of anesthesia.

Blink-related EEG oscillations are neurophysiological indicators of subconcussive head impacts in female soccer players: a preliminary study

Introduction

Repetitive subconcussive head impacts can lead to subtle neural changes and functional consequences on brain health. However, the objective assessment of these changes remains limited. Resting state blink-related oscillations (BROs), recently discovered neurological responses following spontaneous blinking, are explored in this study to evaluate changes in BRO responses in subconcussive head impacts.

Methods

We collected 5-min resting-state electroencephalography (EEG) data from two cohorts of collegiate athletes who were engaged in contact sports (SC) or non-contact sports (HC). Video recordings of all on-field activities were conducted to determine the number of head impacts during games and practices in the SC group.

Results

In both groups, we were able to detect a BRO response. Following one season of games and practice, we found a strong association between the number of head impacts sustained by the SC group and increases in delta and beta spectral power post-blink. There was also a significant difference between the two groups in the morphology of BRO responses, including decreased peak-to-peak amplitude of response over left parietal channels and differences in spectral power in delta and alpha frequency range post-blink.

Discussion

Our preliminary results suggest that the BRO response may be a useful biomarker for detecting subtle neural changes resulting from repetitive head impacts. The clinical utility of this biomarker will need to be validated through further research with larger sample sizes, involving both male and female participants, using a longitudinal design.

Implementing Effective Noise Reduction Techniques in Implantable NIRS Sensors

Near-Infrared Spectroscopy (NIRS) is a noninvasive optical method widely used for evaluating tissue hemodynamics and various physiological characteristics. Despite its advantages, NIRS faces limitations in light sampling depth and spatial resolution, which has led to the development of implantable NIRS sensors. However, these implantable sensors are prone to Common-Mode Voltage (CMV) interference due to their increased sensor-to-tissue capacitance, which can compromise the signal-to-noise ratio and accuracy of measurements.In this paper, we present a novel active CMV reduction technique that enhances the signal-to-noise ratio of NIRS signals. We propose an electrical model of a patient's body and NIRS sensor to characterize the CMV interference and the active CMV cancellation (ACC) electronic circuit. The ACC circuit measures CMV through a common-mode amplifier, which then inverts and introduces the amplified signal to the patient's body via an additional surface electrode. This technique effectively attenuates the CMV (50 and 60 Hz) by 80 to 90 dB, significantly improving the signal quality without causing system instability.The method has been validated through both analytical simulations and experimental measurements, demonstrating the circuit's ability to suppress CMV within a bandwidth of 0.1 to 100 Hz. Experimental verification of the active noise cancellation method was conducted by recording data from the fingertip and palm, showing effective suppression of the CMV. The proposed method has substantial clinical relevance as it enhances the reliability and accuracy of implantable NIRS sensors, enabling more precise monitoring of internal organs and improved patient care.

Detection of spreading depression features from the scalp of epileptic patients

Spreading depression (SD), a pathological cortical negative DC potential, is caused by an elevation of potassium ions in the extracellular space. This leads to a transient relocation of ions within neurons and a slow spread through brain tissue. Our previous research established a correlation between scalp SD and seizures in patients with intractable epilepsy using our novel electroencephalography (EEG). In this study, we enhanced our EEG system by incorporating a Near-infrared spectroscopy (NIRS) module for multi-modal EEG-NIRS measurements. The aim is to provide an investigation into the defining characteristics and methods for detecting SD.Clinical Relevance-: The detection of SD serves as a novel biomarker for epilepsy, capable of forewarning seizures within a time range from 10 secs to 30 min. This detection plays a crucial role in predicting and preventing seizures and providing diagnostic information for drug-resistant epilepsy patients.

Neonatal Face and Facial Landmark Detection from Video Recordings

This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection. Utilising three publicly available datasets of neonates in the clinical environment, 366 images (258 subjects) and 89 (66 subjects) were annotated for training and testing, respectively. Transfer learning was applied to two YOLO-based models, with input training images augmented with random horizontal flipping, photo-metric colour distortion, translation and scaling during each training epoch. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms.Clinical relevance- Accurate face and facial landmark detection provides an automated and non-contact option to assist in video-based neonatal health applications.

Concurrent recordings of slow DC-potentials and epileptiform discharges: novel EEG amplifier and signal processing techniques

Ionic currents within the brain generate voltage oscillations consist of slow and rapid fluctuations. These bioelectrical activities include ultra-low frequency electroencephalograms (DC-EEG, frequency less than 0.1Hz) and conventional clinical electroencephalograms (AC-EEG, 0.5 to 70Hz). Although AC-EEG is commonly used for diagnosing epilepsy, recent studies indicate that DC-EEG is an essential frequency component of EEG and can provide valuable information for analyzing epileptiform discharges. During conventional EEG recordings, DC-EEG is censored by applying high-pass filtering to i) obliterate slow-wave artifacts, ii) eliminate the bioelectrodes' half-cell potential asymmetrical changes in ultralow-low frequency, and iii) prevent instrument saturation. Spreading depression (SD), which is the most prolonged fluctuation in DC-EEG, may be associated with epileptiform discharges. However, recording of SD signals from the scalp's surface can be challenging due to the filtering effect and non-neuronal slow shift potentials. In this study, we describe a novel technique to extend the frequency bandwidth of surface EEG to record SD signals. The method includes novel instrumentation, appropriate bioelectrodes, and efficient signal-processing techniques. To evaluate the accuracy of our approach, we performed a simultaneous surface recording of DC- and AC-EEG from epileptic patients during long-term video EEG monitoring, which provide a promising tool for diagnosis of epilepsy. DATA AVAILABILITY STATEMENT: The data presented in this study are available on request.

Choosing blood pressure thresholds to inform pregnancy care in the community: An analysis of cluster trials

OBJECTIVE

To inform digital health design by evaluating diagnostic test properties of antenatal blood pressure (BP) outputs and levels to identify women at risk of adverse outcomes.

DESIGN

Planned secondary analysis of cluster randomised trials.

SETTING

India, Pakistan, Mozambique.

POPULATION

Women with in-community BP measurements and known pregnancy outcomes.

METHODS

Blood pressure was defined by its outputs (systolic and/or diastolic, systolic only, diastolic only or mean arterial pressure [calculated]) and level: normotension-1 (<135/85 mmHg), normotension-2 (135-139/85-89 mmHg), non-severe hypertension (140-149/90-99 mmHg; 150-154/100-104 mmHg; 155-159/105-109 mmHg) and severe hypertension (≥160/110 mmHg). Dose-response (adjusted risk ratio [aRR]) and diagnostic test properties (negative [-LR] and positive [+LR] likelihood ratios) were estimated.

MAIN OUTCOME MEASURES

Maternal/perinatal composites of mortality/morbidity.

RESULTS

Among 21 069 pregnancies, different BP outputs had similar aRR, -LR, and +LR for adverse outcomes. No BP level (even normotension-1) was associated with low risk (all -LR ≥0.20). Across outcomes, risks rose progressively with higher BP levels above normotension-1. For each of maternal central nervous system events and stillbirth, BP ≥155/105 mmHg showed at least good diagnostic test performance (+LR ≥5.0) and BP ≥135/85 mmHg at least fair performance, similar to BP ≥140/90 mmHg (+LR 2.0-4.99).

CONCLUSIONS

In the community, normal BP values do not provide reassurance about subsequent adverse outcomes. Given the similar performance of BP cut-offs of 135/85 and 140/90 mmHg for hypertension, and 155/105 and 160/110 mmHg for severe hypertension, digital decision support for women in the community should consider using these lower thresholds.

Effective pandemic policy design through feedback does not need accurate predictions

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions.

Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1 min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1 min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively. Clinical relevance--- This paper investigates the feasibility of digital stethoscope recorded chest sounds for early detection of respiratory distress in term newborn babies, to enable timely treatment and management.

Model-plant mismatch detection for cross-directional processes

This paper presents a two-component framework to detect model-plant mismatch (MPM) in cross-directional (CD) processes on paper machines under model-predictive control. First, routine operating data is used for system identification in closed loop; second, a one-class support vector machine (SVM) is trained to predict MPM. The iterative identification method alternates between identifying the finite impulse response coefficients of the spatial and temporal models. It converges, and the parameter estimates are asymptotically consistent. Coefficient estimates drawn from normal operation are used to train a one-class SVM, which then detects model-plant mismatch in subsequent routine operation. This approach applies to routine operating data without requiring external excitations. It can also distinguish mismatches in the process model from changes in the noise model. Examples of CD processes on paper machines are provided to verify the effectiveness of both components.

A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation

Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.

The Effect of Low-Dose Intraoperative Ketamine on Closed-Loop-Controlled General Anesthesia: A Randomized Controlled Equivalence Trial

BACKGROUND

Closed-loop control of propofol-remifentanil anesthesia using the processed electroencephalography depth-of-hypnosis index provided by the NeuroSENSE monitor (WAVCNS) has been previously described. The purpose of this placebo-controlled study was to evaluate the performance (percentage time within ±10 units of the setpoint during the maintenance of anesthesia) of a closed-loop propofol-remifentanil controller during induction and maintenance of anesthesia in the presence of a low dose of ketamine.

METHODS

Following ethical approval and informed consent, American Society of Anesthesiologist (ASA) physical status I-II patients aged 19-54 years, scheduled for elective orthopedic surgery requiring general anesthesia for >60 minutes duration, were enrolled in a double-blind randomized, placebo-controlled, 2-group equivalence trial. Immediately before induction of anesthesia, participants in the ketamine group received a 0.25 mg·kg-1 bolus of intravenous ketamine over 60 seconds followed by a continuous 5 µg·kg-1·min-1 infusion for up to 45 minutes. Participants in the control group received an equivalent volume of normal saline. After the initial study drug bolus, closed-loop induction of anesthesia was initiated; propofol and remifentanil remained under closed-loop control until the anesthetic was tapered and turned off at the anesthesiologist's discretion. An equivalence range of ±8.99% was assumed for comparing controller performance.

RESULTS

Sixty patients participated: 41 males, 54 ASA physical status I, with a median (interquartile range [IQR]) age of 29 [23, 38] years and weight of 82 [71, 93] kg. Complete data were available from 29 cases in the ketamine group and 27 in the control group. Percentage time within ±10 units of the WAVCNS setpoint was median [IQR] 86.6% [79.7, 90.2] in the ketamine group and 86.4% [76.5, 89.8] in the control group (median difference, 1.0%; 95% confidence interval [CI] -3.6 to 5.0). Mean propofol dose during maintenance of anesthesia for the ketamine group was higher than for the control group (median difference, 24.9 µg·kg-1·min-1; 95% CI, 6.5-43.1; P = .005).

CONCLUSIONS

Because the 95% CI of the difference in controller performance lies entirely within the a priori equivalence range, we infer that this analgesic dose of ketamine did not alter controller performance. Further study is required to confirm the finding that mean propofol dosing was higher in the ketamine group, and to investigate the implication that this dose of ketamine may have affected the WAVCNS.

Robust PID control of propofol anaesthesia: Uncertainty limits performance, not PID structure

BACKGROUND AND OBJECTIVE

New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter.

METHODS

The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison.

RESULTS

Two robust filtered PID controllers for the entire model set, as well as individual-specific PID and Youla controllers, were optimised. All considered designs resulted in similar frequency response characteristics. The performance improvement associated with the Youla controllers was not significant compared to the individually tuned filtered PID controllers. The difference in performance between controllers synthesized for the model set and for individual models was significantly larger than the performance difference between the individual-specific PID and Youla controllers. The different controllers were evaluated in simulation. Although all of them showed clinically acceptable results, the robust solutions provided slower responses.

CONCLUSION

Taking the same clinical and technical considerations into account for the optimisation of the different controllers, the design of individual-specific solutions resulted in only marginal differences in performance when comparing an optimal Youla parameter and its optimal filtered PID counterpart. The inter-patient variability is much more detrimental to performance than the limitations imposed by the simple structure of the filtered PID controller.

Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications

With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This paper proposes a new method to objectively and automatically assess the signal quality to improve the accuracy and reliability of heart rate (HR) and breathing rate (BR) estimation from noisy neonatal chest sounds. A total of 88 10-second long chest sounds were taken from 76 preterm and full-term babies. Six annotators independently assessed the signal quality, number of detectable beats, and breathing periods from these recordings. For quality classification, 187 and 182 features were extracted from heart and lung sounds, respectively. After feature selection, class balancing, and hyperparameter optimization, a dynamic binary classification model was trained. Then HR and BR were automatically estimated from the chest sound and several approaches were compared.The results of subject-wise leave-one-out cross-validation, showed that the model distinguished high and low quality recordings in the test set with 96% specificity, 81% sensitivity and 93% accuracy for heart sounds, and 86% specificity, 69% sensitivity and 82% accuracy for lung sounds. The HR and BR estimated from high quality sounds resulted in significantly less median absolute error (4 bpm and 12 bpm difference, respectively) compared to those from low quality sounds. The methods presented in this work, facilitates automated neonatal chest sound auscultation for future telehealth applications.

Individualized closed-loop anesthesia through patient model partitioning

Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, inter-patient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic-pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance-The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.

The effect of cardiac output on the pharmacokinetics and pharmacodynamics of propofol during closed-loop induction of anesthesia

BACKGROUND AND OBJECTIVE

Intraoperative hemodynamic stability is essential to safety and post-operative well-being of patients and should be optimized in closed-loop control of anesthesia. Cardiovascular changes inducing variations in pharmacokinetics may require dose modification. Rigorous investigational tools can strengthen current knowledge of the anesthesiologists and support clinical practice. We quantify the cardiovascular response of high-risk patients to closed-loop anesthesia and propose a new application of physiologically-based pharmacokinetic-pharmacodynamic (PBPK-PD) simulations to examine the effect of hemodynamic changes on the depth of hypnosis (DoH).

METHODS

We evaluate clinical hemodynamic changes in response to anesthesia induction in high-risk patients from a study on closed-loop anesthesia. We develop and validate a PBPK-PD model to simulate the effect of changes in cardiac output (CO) on plasma levels and DoH. The wavelet-based anesthetic value for central nervous system monitoring index (WAV_CNS) is used as clinical end-point of propofol hypnotic effect.

RESULTS

The median (interquartile range, IQR) changes in CO and arterial pressure (AP), 3 min after induction of anesthesia, are 22.43 (14.82-36.0) % and 26.60 (22.39-35.33) % respectively. The decrease in heart rate (HR) is less marked, i.e. 8.82 (4.94-12.68) %. The cardiovascular response is comparable or less enhanced than in manual propofol induction studies. PBPK simulations show that the marked decrease in CO coincides with high predicted plasma levels and deep levels of hypnosis, i.e. WAV_CNS < 40. PD model identification is improved using the PBPK model rather than a standard three-compartment PK model. PD simulations reveal that a 30% drop in CO can cause a 30% change in WAV_CNS.

CONCLUSIONS

Significant CO drops produce increased predicted plasma concentrations corresponding to deeper anesthesia, which is potentially dangerous for elderly patients. PBPK-PD model simulations allow studying and quantifying these effects to improve clinical practice.

Effect of ketamine on the NeuroSENSE WAVCNS during propofol anesthesia; a randomized feasibility trial

Dose-dependent effects of ketamine on processed electroencephalographic depth-of-hypnosis indices have been reported. Limited data are available for the NeuroSENSE WAV_CNS index. Our aim was to establish the feasibility of closed-loop propofol-remifentanil anesthesia guided by the WAV_CNS index in the presence of an analgesic dose of ketamine. Thirty ASA I-II adults, 18-54 years, requiring general anesthesia for anterior cruciate ligament surgery were randomized to receive: full-dose [ketamine, 0.5 mg kg^-1 initial bolus, 10 mcg kg^-1 min^-1 infusion] (recommended dose for postoperative pain management); half-dose [ketamine, 0.25 mg kg^-1 bolus, 5 mcg kg^-1 min^-1 infusion]; or control [no ketamine]. After the ketamine bolus, patients received 1.0 mcg kg^-1 remifentanil over 30 s, then 1.5 mg kg^-1 propofol over 30 s, followed by manually-adjusted propofol-remifentanil anesthesia. The WAV_CNS was > 60 for 7/9 patients in the full-dose group at 7 min after starting the propofol infusion. This was inconsistent with clinical observations of depth-of-hypnosis and significantly higher than control (median difference [MD] 17.0, 95% confidence interval [CI] 11.4-26.8). WAV_CNS was median [interquartile range] 49.3 [42.2-62.6] in the half-dose group, and not different to control (MD 5.1, 95% CI - 4.9 to 17.9). During maintenance of anesthesia, the WAV_CNS was higher in the full-dose group compared to control (MD 14.7, 95% CI 10.2-19.2) and in the half-dose group compared to control (MD 11.4, 95% CI 4.7-20.4). The full-dose of ketamine recommended for postoperative pain management had a significant effect on the WAV_CNS. This effect should be considered when using the WAV_CNS to guide propofol-remifentanil dosing.Trial Registration ClinicalTrails.gov No. NCT02908945.

Evaluating NeuroSENSE for assessing depth of hypnosis during desflurane anesthesia: an adaptive, randomized-controlled trial

PURPOSE

Processed electroencephalography (EEG) monitors support depth-of-hypnosis assessment during anesthesia. This randomized-controlled trial investigated the performance of the NeuroSENSE electroencephalography (EEG) monitor to determine whether its wavelet anesthetic value for central nervous system (WAVCNS) index distinguishes consciousness from unconsciousness during induction of anesthesia (as assessed by the anesthesiologist) and emergence from anesthesia (indicated by patient responsiveness), and whether it correlates with changes in desflurane minimum alveolar concentration (MAC) during maintenance of anesthesia.

METHODS

EEG was collected using a fronto-temporal bilateral montage. The WAVCNS was continuously recorded by the NeuroSENSE monitor, to which the anesthesiologist was blinded. Anesthesia was induced with propofol/remifentanil and maintained with desflurane, with randomized changes of -0.4, 0, or +0.4 MAC every 7.5 min within the 0.8-1.6 MAC range, if clinically acceptable to the anesthesiologist. During emergence from anesthesia, desflurane was stepped down by 0.2 MAC every five minutes.

RESULTS

Data from 75 patients with a median [interquartile range] age of 41[35-52] yr were obtained. The WAVCNS distinguished consciousness from unconsciousness as assessed by the anesthesiologist, with area under the receiver operating characteristic curve of 99.5% (95% confidence interval [CI], 98.5 to 100.0) at loss of consciousness and 99.4% (95% CI, 98.5 to 100.0) at return of consciousness. Bilateral WAVCNS changes correlated with desflurane concentrations, with -8.0 and -8.6 WAVCNS units, respectively, per 1 MAC change in the 0.8-1.6 MAC range during maintenance of anesthesia and -10.0 and -10.5 WAVCNS units, respectively, in the 0.4-1.6 MAC range including emergence from anesthesia.

CONCLUSION

The NeuroSENSE monitor can reliably discriminate between consciousness and unconsciousness, as assessed by the anesthesiologist, during induction of anesthesia and with a lower level of reliability during emergence from anesthesia. The WAVCNS correlates with desflurane concentration but plateaus at higher concentrations, similar to other EEG monitors, which suggests limited utility to titrate higher concentrations of anesthetic vapour.

TRIAL REGISTRATION

clinicaltrials.gov, NCT02088671; registered 17 March, 2014.

Optical Assessment of Spinal Cord Tissue Oxygenation Using a Miniaturized Near Infrared Spectroscopy Sensor

Despite advances in the treatment of acute spinal cord injury (SCI), measures to mitigate permanent neurological deficits in affected patients are limited. Immediate post-trauma hemodynamic management of patients, to maintain blood supply and improve oxygenation to the injured spinal cord, is currently one aspect of critical care which clinicians can utilize to improve neurological outcomes. However, without a way to monitor the response of spinal cord hemodynamics and oxygenation in real time, optimizing hemodynamic management is challenging and limited in scope. This study aims to investigate the feasibility and validity of using a miniaturized multi-wavelength near-infrared spectroscopy (NIRS) sensor for direct transdural monitoring of spinal cord oxygenation in an animal model of acute SCI. Nine Yorkshire pigs underwent a weight-drop T10 contusion-compression injury and received episodes of ventilatory hypoxia and alterations in mean arterial pressure (MAP). Spinal cord hemodynamics and oxygenation were monitored throughout by a non-invasive transdural NIRS sensor, as well as an invasive intraparenchymal sensor as a comparison. NIRS parameters of tissue oxygenation were highly correlated with intraparenchymal measures of tissue oxygenation. In particular, during periods of hypoxia and MAP alterations, changes of NIRS-derived spinal cord oxygenated hemoglobin and tissue oxygenation percentage corresponded well with the changes in spinal cord oxygen partial pressures measured by the intraparenchymal sensor. Our data confirm that during hypoxic episodes and as changes occur in the MAP, non-invasive NIRS can detect and measure real-time changes in spinal cord oxygenation with a high degree of sensitivity and specificity.

Optimizing Robust PID Control of Propofol Anesthesia for Children: Design and Clinical Evaluation

OBJECTIVE

The goal of this paper was to optimize robust PID control for propofol anesthesia in children aged 5-10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness.

METHODS

We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce the interpatient variability and a new robust PID controller was designed using an optimization-based method. We evaluated this optimized design in a clinical study involving 16 new cases.

RESULTS

The optimized controller design achieved the performance predicted in simulation studies in the design stage. Time of induction of anesthesia was median [Q1, Q3] 3.7 [2.3, 4.1] min and the achieved global score was 13.4 [9.9, 16.8].

CONCLUSION

Allometric scaling reduces the interpatient variability in this age group and allows for improved closed-loop performance. The uncertainty described by the model set, the predicted closed-loop responses, and the predicted robustness margins are realistic. The system meets the design objectives of improved speed of induction of anesthesia while maintaining robustness and improving clinically relevant system behavior.

SIGNIFICANCE

Control system optimization and ongoing system improvements are essential to the development of a clinically relevant commercial device. This paper demonstrates the validity of our approach, including system modeling, controller optimization, and pre-clinical testing in simulation.

A Multi-Week Assessment of a Mobile Exergame Intervention in an Elementary School

BACKGROUND

Exergaming is potentially useful to promote physical activity in children; however, long-term effectiveness is unclear. MobileKids Monster Manor (MKMM) is a mobile exergame developed with the help of young advisors. The game wirelessly transmits physical activity data from an accelerometer to a mobile device. Players' steps are redeemed for in-game rewards, for example, new characters.

OBJECTIVE

First, to evaluate whether increased physical activity previously observed in a 1-week intervention is sustained over a 2-week intervention and 1-week follow-up, and second, to compare impact in schools within different socioeconomic environments.

METHODS

Thirty-seven elementary school students participated in a 4-week randomized controlled study (1-week baseline; 2-week intervention [with only the Game group receiving MKMM]; and 1-week follow-up). All participants wore a Tractivity^® accelerometer throughout. Linear mixed models were applied to assess sustainability; a second 42-children-based dataset and age-/sex-adjusted linear regression models were used to compare effect across socioeconomic environments.

RESULTS

In the first week of intervention, the Game group compared to the Control group showed a greater increase in physical activity (of 1,758 steps/day [95% confidence interval, CI = 133-3,385] and 31 active minutes/day [95% CI = 4-59]), relative to baseline (13,986 steps/day; 231 active minutes/day). However, this was not sustained in the second intervention week or follow-up. The school within a lower socioeconomic status environment showed lower baseline activity and the 1-week intervention resulted in a greater increase relative to baseline (3,633 steps/day more [95% CI = 1,281-5,985]).

CONCLUSION

MKMM could be a useful short-term physical activity promotion tool; however, effectiveness may decrease as novelty diminishes.

Pediatric pulse oximetry-based OSA screening at different thresholds of the apnea-hypopnea index with an expression of uncertainty for inconclusive classifications

BACKGROUND

Assessments of pediatric obstructive sleep apnea (OSA) are underutilized across Canada due to a lack of resources. Polysomnography (PSG) measures OSA severity through the average number of apnea/hypopnea events per hour (AHI), but is resource intensive and requires a specialized sleep laboratory, which results in long waitlists and delays in OSA detection. Prompt diagnosis and treatment of OSA are crucial for children, as untreated OSA is linked to behavioral deficits, growth failure, and negative cardiovascular consequences. We aim to assess the performance of a portable pediatric OSA screening tool at different AHI cut-offs using overnight smartphone-based pulse oximetry.

MATERIAL AND METHODS

Following ethics approval and informed consent, children referred to British Columbia Children's Hospital for overnight PSG were recruited for two studies including 160 and 75 children, respectively. An additional smartphone-based pulse oximeter sensor was used in both studies to record overnight pulse oximetry [SpO₂ and photoplethysmogram (PPG)] alongside the PSG. Features characterizing SpO₂ dynamics and heart rate variability from pulse peak intervals of the PPG signal were derived from pulse oximetry recordings. Three multivariate logistic regression screening models, targeted at three different levels of OSA severity (AHI ≥ 1, 5, and 10), were developed using stepwise-selection of features using the Bayesian information criterion (BIC). The "Gray Zone" approach was also implemented for different tolerance values to allow for more precise detection of children with inconclusive classification results.

RESULTS

The optimal diagnostic tolerance values defining the "Gray Zone" borders (15, 10, and 5, respectively) were selected to develop the final models to screen for children at AHI cut-offs of 1, 5, and 10. The final models evaluated through cross-validation showed good accuracy (75%, 82% and 89%), sensitivity (80%, 85% and 82%) and specificity (65%, 79% and 91%) values for detecting children with AHI ≥ 1, AHI ≥ 5 and AHI ≥ 10. The percentage of children classified as inconclusive was 28%, 38% and 16% for models detecting AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10, respectively.

CONCLUSIONS

The proposed pulse oximetry-based OSA screening tool at different AHI cut-offs may assist clinicians in identifying children at different OSA severity levels. Using this tool at home prior to PSG can help with optimizing the limited resources for PSG screening. Further validation with larger and more heterogeneous datasets is required before introducing in clinical practice.

Symbolic dynamics to enhance diagnostic ability of portable oximetry from the phone oximeter in the detection of paediatric sleep apnoea

OBJECTIVE

This study is aimed at assessing symbolic dynamics as a reliable technique to characterise complex fluctuations of portable oximetry in the context of automated detection of childhood obstructive sleep apnoea-hypopnoea syndrome (OSAHS).

APPROACH

Nocturnal oximetry signals from 142 children with suspected OSAHS were acquired using the Phone Oximeter: a portable device that integrates a pulse oximeter with a smartphone. An apnoea-hypopnoea index (AHI) ≥5 events/h from simultaneous in-lab polysomnography was used to confirm moderate-to-severe childhood OSAHS. Symbolic dynamics was used to parameterise non-linear changes in the overnight oximetry profile. Conventional indices, anthropometric measures, and time-domain linear statistics were also considered. Forward stepwise logistic regression was used to obtain an optimum feature subset. Logistic regression (LR) was used to identify children with moderate-to-severe OSAHS.

MAIN RESULTS

The histogram of 3-symbol words from symbolic dynamics showed significant differences (p <0.01) between children with AHI <5 events/h and moderate-to-severe patients (AHI ≥5 events/h). Words representing increasing oximetry values after apnoeic events (re-saturations) showed relevant diagnostic information. Regarding the performance of individual characterization approaches, the LR model composed of features from symbolic dynamics alone reached a maximum performance of 78.4% accuracy (65.2% sensitivity; 86.8% specificity) and 0.83 area under the ROC curve (AUC). The classification performance improved combining all features. The optimum model from feature selection achieved 83.3% accuracy (73.5% sensitivity; 89.5% specificity) and 0.89 AUC, significantly (p-value <0.01) outperforming the other models.

SIGNIFICANCE

Symbolic dynamics provides complementary information to conventional oximetry analysis enabling reliable detection of moderate-to-severe paediatric OSAHS from portable oximetry.

Extracting Instantaneous Respiratory Rate From Multiple Photoplethysmogram Respiratory-Induced Variations

In this study, we proposed a novel method for extracting the instantaneous respiratory rate (IRR) from the pulse oximeter photoplethysmogram (PPG). The method was performed in three main steps: (1) a time-frequency transform called synchrosqueezing transform (SST) was used to extract the respiratory-induced intensity, amplitude and frequency variation signals from PPG, (2) the second SST was applied to each extracted respiratory-induced variation signal to estimate the corresponding IRR, and (3) the proposed peak-conditioned fusion method then combined the IRR estimates to calculate the final IRR. We validated the implemented method with capnography and nasal/oral airflow as the reference RR using the limits of agreement (LOA) approach. Compared to simple fusion and single respiratory-induced variation estimations, peak-conditioned fusion shows better performance. It provided a bias of 0.28 bpm with the 95% LOAs ranging from −3.62 to 4.17, validated against capnography and a bias of 0.04 bpm with the 95% LOAs ranging from −5.74 to 5.82, validated against nasal/oral airflow. This algorithm would expand the functionality of a conventional pulse oximetry beyond the measurement of heart rate and oxygen saturation to measure the respiratory rate continuously and instantly.

Clinician-Driven Design of VitalPAD-An Intelligent Monitoring and Communication Device to Improve Patient Safety in the Intensive Care Unit

The pediatric intensive care unit (ICU) is a complex environment, in which a multidisciplinary team of clinicians (registered nurses, respiratory therapists, and physicians) continually observe and evaluate patient information. Data are provided by multiple, and often physically separated sources, cognitive workload is high, and team communication can be challenging. Our aim is to combine information from multiple monitoring and therapeutic devices in a mobile application, the VitalPAD, to improve the efficiency of clinical decision-making, communication, and thereby patient safety. We observed individual ICU clinicians, multidisciplinary rounds, and handover procedures for 54 h to identify data needs, workflow, and existing cognitive aid use and limitations. A prototype was developed using an iterative participatory design approach; usability testing, including general and task-specific feedback, was obtained from 15 clinicians. Features included map overviews of the ICU showing clinician assignment, patient status, and respiratory support; patient vital signs; a photo-documentation option for arterial blood gas results; and team communication and reminder functions. Clinicians reported the prototype to be an intuitive display of vital parameters and relevant alerts and reminders, as well as a user-friendly communication tool. Future work includes implementation of a prototype, which will be evaluated under simulation and real-world conditions, with the aim of providing ICU staff with a monitoring device that will improve their daily work, communication, and decision-making capacity. Mobile monitoring of vital signs and therapy parameters might help improve patient safety in wards with single-patient rooms and likely has applications in many acute and critical care settings.

Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry

Sleep apnea, characterized by frequent pauses in breathing during sleep, poses a serious threat to the healthy growth and development of children. Polysomnography (PSG), the gold standard for sleep apnea diagnosis, is resource intensive and confined to sleep laboratories, thus reducing its accessibility. Pulse oximetry alone, providing blood oxygen saturation (SpO2) and blood volume changes in tissue (PPG), has the potential to identify children with sleep apnea. Thus, we aim to develop a tool for at-home sleep apnea screening that provides a detailed and automated 30 sec epoch-by-epoch sleep apnea analysis. We propose to extract features characterizing pulse oximetry (SpO2 and pulse rate variability [PRV], a surrogate measure of heart rate variability) to create a multivariate logistic regression model that identifies epochs containing apnea/hypoapnea events. Overnight pulse oximetry was collected using a smartphone-based pulse oximeter, simultaneously with standard PSG from 160 children at the British Columbia Children's hospital. The sleep technician manually scored all apnea/hypoapnea events during the PSG study. Based on these scores we labeled each epoch as containing or not containing apnea/hypoapnea. We randomly divided the subjects into training data (40%), used to develop the model applying the LASSO method, and testing data (60%), used to validate the model. The developed model was assessed epoch-by-epoch for each subject. The test dataset had a median area under the receiver operating characteristic (ROC) curve of 81%; the model provided a median accuracy of 74% sensitivity of 75%, and specificity of 73% when using a risk threshold similar to the percentage of apnea/hypopnea epochs. Thus, providing a detailed epoch-by-epoch analysis with at-home pulse oximetry alone is feasible with accuracy, sensitivity and specificity values above 73% However, the performance might decrease when analyzing subjects with a low number of apnea/hypoapnea events.

Detecting obstructive sleep apnea in children by self-affine visualization of oximetry

Obstructive sleep apnea (OSA), characterized by cessations of breathing during sleep due to upper airway collapse, can affect the healthy growth and development of children. The gold standard for OSA diagnosis, polysomnography(PSG), is expensive and resource intensive, resulting in long waiting lists to perform a PSG. Previously, we investigated the time-frequency analysis of blood oxygen saturation (SpO₂) to screen for OSA. We used overnight pulse oximetry from 146 children, collected using a smartphone-based pulse oximeter (Phone Oximeter), simultaneously with standard PSG. Sleep technicians manually scored PSG and provided the average of apnea/hypoapnea events per hour (AHI). In this study, we proposed an alternative method for analyzing SpO₂, in which a set of contracting transformations form a self-affine set with a 2D attractor, previously developed for qualitative visualization of the photoplethysmogram and electroencephalogram. We applied this technique to the overnight SpO₂ signal from individual patients and extracted features based on the distribution of points (radius and angle) in the visualization. The cloud of points in children without OSA (NonOSA) was more confined than in children with OSA, which was reflected by more empty pixels (radius and angles). The maximum value, skewness and standard deviation of the distribution of points located at different radius and angles were significantly (Bonferroni corrected) higher in NonOSA compared to OSA children. To detect OSA defined at different levels (AHI≥5, AHI≥10 and AHI≥15), three multivariate logistic regression models were implemented using a stepwise feature selection and internally validated through bootstrapping. The models (AHI≥5, AHI≥10, AHI≥15), consisting of 3, 4 and 1 features respectively, provided a bootstrap-corrected AUC of 73%, 81%, 73%. Thus, applying this visualization to nocturnal SpO₂ could yield both visual and quantitative information that might be useful for screening children for OSA.

Patient and health care professional perspectives on using telehealth to deliver pulmonary rehabilitation

The objective of this study was to identify the necessary features of pulmonary telerehabilitation (P-TR) from the perspectives of individuals living with chronic lung disease and health care professionals (HCPs) who deliver pulmonary rehabilitation (PR). Focus groups were carried out with patients (n = 26) and HCPs (n = 26) to elicit and explore their opinions about the critical elements of in-person PR and ideas for how these elements could be supported using technology. A questionnaire was used to assess technology use, PR experience, and general health status. Four key elements of PR were identified as critical to P-TR: the social aspect of PR; communicating with HCPs for education and support; using biosensors for monitoring and promoting self-knowledge; and the evolution of support with progress over time. A range of technology-enabled devices and programs were suggested as means to recreate aspects of these integral elements. Consultations with patients and HCPs suggest that users are interested in technology and want to ensure it recreates the important aspects of PR. Patients and HCPs identified similar key elements for P-TR. The opinions and suggestions of patients and HCPs should be the driving force of innovation if P-TR is to succeed in improving health outcomes.

Assessment of a Mobile Game ("MobileKids Monster Manor") to Promote Physical Activity Among Children

OBJECTIVE

The majority of children in North America are not meeting current physical activity guidelines. The purpose of this study was to evaluate the impact of a mobile phone game ("MobileKids Monster Manor") as a tool to promote voluntary physical activity among children.

MATERIALS AND METHODS

The game integrates data from an accelerometer-based activity monitor (Tractivity(®); Kineteks Corp., Vancouver, BC, Canada) wirelessly connected to a phone and was developed with the involvement of a team of young advisors (KidsCan Initiative: Involving Youth as Ambassadors for Research). Fifty-four children 8-13 years old completed a week of baseline data collection by wearing an accelerometer but receiving no feedback about their activity levels. The 54 children were then sequentially assigned to two groups: One group played "MobileKids Monster Manor," and the other received daily activity feedback (steps and active minutes) via an online program. The physical activity (baseline and intervention weeks) was measured using the activity monitor and compared using two-way repeated-measures analysis of variance (intervention×time).

RESULTS

Forty-seven children with a body mass index (BMI) z-score of 0.35±1.18 successfully completed the study. Significant (P=0.01) increases in physical activity were observed during the intervention week in both the game and feedback groups (1191 and 796 steps/day, respectively). In the game group, greater physical activity was demonstrated in children with higher BMI z-score, showing 964 steps/day more per BMI z-score unit (P=0.03; 95 percent confidence interval of 98 to 1829).

CONCLUSIONS

Further investigation is required to confirm that our game design promotes physical activity.

Closed-loop target-controlled infusion systems: stability and performance aspects

OBJECTIVE

This article addresses the design of a robust autopilot for the delivery of intravenous anesthesia drugs.

METHODS

A mathematical framework that expresses the pharmacological variability of a patient population into uncertainty bounds is proposed. These bounds can be effectively used to tune the parameters of a controller to ensure its stability, a key design aspect related to the safety of the overall system.

RESULTS

The proposed method is applied to the control of propofol, a powerful hypnotic agent used for sedation and anesthesia. Simulations show that the controller remains stable for all patients considered and that performance are clinically acceptable.

CONCLUSION

This methodology can be an important step forward in the design and regulatory approval of such systems.

Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children

Obstructive sleep apnea (OSA) disrupts normal ventilation during sleep and can lead to serious health problems in children if left untreated. Polysomnography, the gold standard for OSA diagnosis, is resource intensive and requires a specialized laboratory. Thus, we proposed to use the Phone Oximeter™, a portable device integrating pulse oximetry with a smartphone, to detect OSA events. As a proportion of OSA events occur without oxygen desaturation (defined as SpO2 decreases ≥ 3%), we suggest combining SpO2 and pulse rate variability (PRV) analysis to identify all OSA events and provide a more detailed sleep analysis. We recruited 160 children and recorded pulse oximetry consisting of SpO2 and plethysmography (PPG) using the Phone Oximeter™, alongside standard polysomnography. A sleep technician visually scored all OSA events with and without oxygen desaturation from polysomnography. We divided pulse oximetry signals into 1-min signal segments and extracted several features from SpO2 and PPG analysis in the time and frequency domain. Segments with OSA, especially the ones with oxygen desaturation, presented greater SpO2 variability and modulation reflected in the spectral domain than segments without OSA. Segments with OSA also showed higher heart rate and sympathetic activity through the PRV analysis relative to segments without OSA. PRV analysis was more sensitive than SpO2 analysis for identification of OSA events without oxygen desaturation. Combining SpO2 and PRV analysis enhanced OSA event detection through a multiple logistic regression model. The area under the ROC curve increased from 81% to 87%. Thus, the Phone Oximeter™ might be useful to monitor sleep and identify OSA events with and without oxygen desaturation at home.

Estimating instantaneous respiratory rate from the photoplethysmogram

The photoplethysmogram (PPG) obtained from pulse oximetry shows the local changes of blood volume in tissues. Respiration induces variation in the PPG baseline due to the variation in venous blood return during each breathing cycle. We have proposed an algorithm based on the synchrosqueezing transform (SST) to estimate instantaneous respiratory rate (IRR) from the PPG. The SST is a combination of wavelet analysis and a reallocation method which aims to sharpen the time-frequency representation of the signal and can provide an accurate estimation of instantaneous frequency. In this application, the SST was applied to the PPG and IRR was detected as the predominant ridge in the respiratory band (0.1 Hz - 1 Hz) in the SST plane. The algorithm was tested against the Capnobase benchmark dataset that contains PPG, capnography, and expert labelled reference respiratory rate from 42 subjects. The IRR estimation accuracy was assessed using the root mean square (RMS) error and Bland-Altman plot. The median RMS error was 0.39 breaths/min for all subjects which ranged from the lowest error of 0.18 breaths/min to the highest error of 13.86 breaths/min. A Bland-Altman plot showed an agreement between the IRR obtained from PPG and reference respiratory rate with a bias of -0.32 and limits agreement of -7.72 to 7.07. Extracting IRR from PPG expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

A delayed functional observer/predictor with bounded-error for depth of hypnosis monitoring

With the motivation of providing safety for a patient under anesthesia, this paper suggests conditions for evaluating the correctness of an available user interface for systems under shared control based on observability and predictability requirements. Situation awareness is necessary for the user to make correct decisions about the inputs. In this article, we develop a technique to investigate the conditions under which an anesthetists can attain situation awareness about a limited but important aspect of anesthesia, namely depth of hypnosis (DOH). Furthermore, we consider that, in practice, to attain situation awareness, the estimation of the task states does not necessarily need to be precise but can be bounded within certain margins. Hence, attaining situation awareness about DOH is modeled as a bounded-error delayed functional observation/prediction. Unless such an observer/predictor exists for a system with a given user-interface, the safety of the operation may be compromised. The suggested technique proves that, in order to provide safety for the patient under anesthesia, it is necessary for the anesthetist to have access to the predictive information from a clinical decision support system.

Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study

OBJECTIVE

Hypoxaemia is a strong predictor of mortality in children. Early detection of deteriorating condition is vital to timely intervention. We hypothesise that measures of pulse oximetry dynamics may identify children requiring hospitalisation. Our aim was to develop a predictive tool using only objective data derived from pulse oximetry and observed respiratory rate to identify children at increased risk of hospital admission.

SETTING

Tertiary-level hospital emergency department in Bangladesh.

PARTICIPANTS

Children under 5 years (n=3374) presenting at the facility (October 2012-April 2013) without documented chronic diseases were recruited. 1-minute segments of pulse oximetry (photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate (HR)) and respiratory rate were collected with a mobile app.

PRIMARY OUTCOME

The need for hospitalisation based on expert physician review and follow-up.

METHODS

Pulse rate variability (PRV) using pulse peak intervals of the PPG signal and features extracted from the SpO2 signal, all derived from pulse oximetry recordings, were studied. A univariate age-adjusted logistic regression was applied to evaluate differences between admitted and non-admitted children. A multivariate logistic regression model was developed using a stepwise selection of predictors and was internally validated using bootstrapping.

RESULTS

Children admitted to hospital showed significantly (p<0.01) decreased PRV and higher SpO2 variability compared to non-admitted children. The strongest predictors of hospitalisation were reduced PRV-power in the low frequency band (OR associated with a 0.01 unit increase, 0.93; 95% CI 0.89 to 0.98), greater time spent below an SpO2 of 98% and 94% (OR associated with 10 s increase, 1.4; 95% CI 1.3 to 1.4 and 1.5; 95% CI 1.4 to 1.6, respectively), high respiratory rate, high HR, low SpO2, young age and male sex. These variables provided a bootstrap-corrected AUC of the receiver operating characteristic of 0.76.

CONCLUSIONS

Objective measurements, easily obtained using a mobile device in low-resource settings, can predict the need for hospitalisation. External validation will be required before clinical adoption.

The Performance of a Mobile Phone Respiratory Rate Counter Compared to the WHO ARI Timer

OBJECTIVE

To compare the accuracy and efficiency of the respiratory rate (RR) RRate mobile application to the WHO ARI Timer.

METHODS

Volunteers used both devices to measure RR from reference videos of infants and children. Measurements were compared using correlation, Bland-Altman analysis, error metrics and time taken.

RESULTS

Measurements with either device were highly correlated to the reference (r = 0.991 and r = 0.982), and to each other (r = 0.973). RRate had a larger bias than the ARI Timer (0.6 vs. 0.04 br/min), but tighter limits of agreement (-4.5 to 3.3 br/min vs. -5.5 to 5.5 br/min). RRate was more accurate than the ARI Timer (percentage error 10.6% vs. 14.8%, root mean square error 2.1 vs. 2.8 br/min and normalized root mean square error 5.6% vs. 7.5%). RRate measurements were 52.7 seconds (95% CI 50.4 s to 54.9 s) faster.

CONCLUSION

During video observations, RRate measured RR quicker with a similar accuracy compared to the ARI Timer.

Evaluation of cardiac modulation in children in response to apnea/hypopnea using the Phone Oximeter(™)

Individuals with sleep disordered breathing (SDB) can experience changes in automatic cardiac regulation as a result of frequent sleep fragmentation and disturbance in normal respiration and oxygenation that accompany most apnea/hypopnea events. In adults, these changes are reflected in enhanced sympathetic and reduced parasympathetic activity. In this study, we examined the autonomic cardiac regulation in children with and without SDB, through spectral and detrended fluctuation analysis (DFA) of pulse rate variability (PRV). PRV was measured from pulse-to-pulse intervals (PPIs) of the photoplethysmogram (PPG) recorded from 160 children using the Phone Oximeter(™) in the standard setting of overnight polysomnography. Spectral analysis of PRV showed the cardiac parasympathetic index (high frequency, HF) was lower (p < 0.01) and cardiac sympathetic indices (low frequency, LF and LF/HF ratio) were higher (p < 0.01) during apnea/hypopnea events for more than 95% of children with SDB. DFA showed the short- and long-range fluctuations of heart rate were more strongly correlated in children with SDB compared to children without SDB. These findings confirm that the analysis of the PPG recorded using the Phone Oximeter(™) could be the basis for a new screening tool for assessing PRV in non-clinical environment.

Development and Internal Validation of a Predictive Model Including Pulse Oximetry for Hospitalization of Under-Five Children in Bangladesh

Background

The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course.

Methods

We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested.

Findings

Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42–2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%).

Conclusion

A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice.

Oxygen saturation resolution influences regularity measurements

The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.

Pulse rate variability compared with Heart Rate Variability in children with and without sleep disordered breathing

Heart Rate Variability (HRV), the variation of time intervals between heartbeats, is one of the most promising and widely used quantitative markers of autonomic activity. Traditionally, HRV is measured as the series of instantaneous cycle intervals obtained from the electrocardiogram (ECG). In this study, we investigated the estimation of variation in heart rate from a photoplethysmography (PPG) signal, called pulse rate variability (PRV), and assessed its accuracy as an estimate of HRV in children with and without sleep disordered breathing (SDB). We recorded raw PPGs from 72 children using the Phone Oximeter, an oximeter connected to a mobile phone. Full polysomnography including ECG was simultaneously recorded for each subject. We used correlation and Bland-Altman analysis for comparing the parameters of HRV and PRV between two groups of children. Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, standard deviation of intervals (SDNN) and the root-mean square of the difference of successive intervals (RMSSD). However Bland-Altman analysis showed a large divergence for LF/HF ratio parameter. In addition, children with SDB had depressed SDNN and RMSSD and elevated LF/HF in comparison to children without SDB. In conclusion, PRV provides the accurate estimate of HRV in time domain analysis but does not reflect precise estimation for parameters in frequency domain.

Oxygen saturation in children with and without obstructive sleep apnea using the phone-oximeter

Obstructive sleep apnea (OSA) in children can lead to daytime sleepiness, growth failure and developmental delay. Polysomnography (PSG), the gold standard to diagnose OSA is highly resource intensive and is confined to the sleep laboratory. In this study we propose to identify children with OSA using blood oxygen saturation (SpO2) obtained from the Phone Oximeter. This portable, in-home device is able to monitor patients over multiple nights, causes less sleep disturbance and facilitates a more natural sleep pattern. The proposed algorithm analyzes the SpO2 signal in the time and frequency domain using a 90-s sliding window. Three spectral parameters are calculated from the power spectral density (PSD) to evaluate the modulation in the SpO2 due to the oxyhemoblobin desaturations. The power P, slope S in the discriminant band (DB), and ratio R between P and total power are calculated for each window. Tendency and variability indices, number of SpO2 desaturations and time spent under 2% or 3% of baseline saturation level are computed for each time window. The statistical distribution of the temporal evolution of all parameters is analyzed to identify 68 children, 30 with OSA and 38 without OSA (nonOSA). This characterization was evaluated by a feature selection based on a linear discriminant. The combination of temporal and spectral parameters provided the best leave one out crossvalidation results with an accuracy of 86.8%, a sensitivity of 80.0%, and a specificity of 92.1% using only 5 parameters. The median of R, mean of P and S and mean and standard deviation of the number of desaturations below 3% of baseline saturation level, were the most representative parameters. Hence, a better knowledge of SpO2 dynamics could help identifying children with OSA with the Phone Oximeter.

Circadian variation of scalp EEG: a novel measure based on wavelet packet transform and differential entropy

We propose a novel entropy-based measure to quantify the circadian variations of scalp electroencephalogram (EEG) by analyzing waking epochs of nap opportunities under an ultradian sleep-wake cycle (USW) protocol. To compute this circadian measure for a nap opportunity, each waking epoch (~1 sec) is decomposed using wavelet packet transform and the relative energy for the desired frequency band (here, 10-12 Hz) is calculated. Then, in a bootstrapping procedure, a shape statistic (skewness or kurtosis) of the relative energy distribution, after each resampling, is computed. Finally, the probability density function of this statistic is estimated, and the corresponding differential entropy is considered as the circadian measure. This measure was evaluated using EEG recordings from 4 healthy subjects during a 72-h USW procedure. According to the results, the proposed measure showed a significant circadian variation both for individual and group data, with peak values occurring near the core body temperature minimum. The performance of the entropy-based measure was also compared with that of two other measures, namely mean energy logarithm and mean energy ratio, revealing the superiority of this measure.

Identification of scalp EEG circadian variation using a novel correlation sum measure

OBJECTIVE

In this paper, we propose a novel method to determine the circadian variation of scalp electroencephalogram (EEG) in both individual and group levels using a correlation sum measure, quantifying self-similarity of the EEG relative energy across waking epochs.

APPROACH

We analysed EEG recordings from central-parietal and occipito-parietal montages in nine healthy subjects undergoing a 72 h ultradian sleep-wake cycle protocol. Each waking epoch (∼ 1 s) of every nap opportunity was decomposed using the wavelet packet transform, and the relative energy for that epoch was calculated in the desired frequency band using the corresponding wavelet coefficients. Then, the resulting set of energy values was resampled randomly to generate different subsets with equal number of elements. The correlation sum of each subset was then calculated over a range of distance thresholds, and the average over all subsets was computed. This average value was finally scaled for each nap opportunity and considered as a new circadian measure.

MAIN RESULTS

According to the evaluation results, a clear circadian rhythm was identified in some EEG frequency ranges, particularly in 4-8 Hz and 10-12 Hz. The correlation sum measure not only was able to disclose the circadian rhythm on the group data but also revealed significant circadian variations in most individual cases, as opposed to previous studies only reporting the circadian rhythms on a population of subjects. Compared to a naive measure based on the EEG absolute energy in the frequency band of interest, the proposed measure showed a clear superiority using both individual and group data. Results also suggested that the acrophase (i.e., the peak) of the circadian rhythm in 10-12 Hz occurs close to the core body temperature minimum.

SIGNIFICANCE

These results confirm the potential usefulness of the proposed EEG-based measure as a non-invasive circadian marker.