Night to night variability of pulse oximetry features in children at home and at the hospital.

Objective. Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea.Approach. Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models.Main results. Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital.Significance. At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children.

A standardized validity assessment protocol for physiological signals from wearable technology: Methodological underpinnings and an application to the E4 biosensor.

Wearable physiological measurement devices for ambulatory research with novel sensing technology are introduced with ever increasing frequency, requiring fast, standardized, and rigorous validation of the physiological signals measured by these devices and their derived parameters. At present, there is a lack of consensus on a standardized protocol or framework with which to test the validity of this new technology, leading to the use of various (often unfit) methods. This study introduces a comprehensive validity assessment protocol for physiological signals (electrodermal activity and cardiovascular activity) and investigates the validity of the E4 wearable (an example of such a new device) on the three levels proposed by the protocol: (1) the signal level, with a cross-correlation; (2) the parameter level, with Bland-Altman plots; and (3) the event level, with the detection of physiological changes due to external stressor levels via event difference plots. The results of the protocol show that the E4 wearable is valid for heart rate, RMSSD, and SD at the parameter and event levels, and for the total amplitude of skin conductance responses at the event level when studying strong sustained stressors. These findings are in line with the prior literature and demonstrate the applicability of the protocol. The validity assessment protocol proposed in this study provides a comprehensive, standardized, and feasible method for assessment of the quality of physiological data coming from new wearable (sensor) technology aimed at ambulatory research.

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.

Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation.

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine. METHODS AND RESULTS: A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (>480 ms), the extended model showed a major drop in false negative classifications of LQTS patients. CONCLUSION: The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS.

Night to night pulse oximetry variability in children with suspected sleep apnea.

Obstructive Sleep Apnea (OSA) is the most common form of sleep-disordered breathing in children. The gold standard to screen for OSA, polysomnography (PSG), requires an overnight stay in the hospital and is resource intensive. The Phone Oximeter is a non-invasive smartphone-based tool to record pulse oximetry. This portable device is able to measure patients over multiple nights while at home, causing less sleep disturbance than PSG and is able to measure night to night variability in sleep. This study analyzed the Screen My Sleep children (SMS) dataset, in which 74 children were monitored over multiple nights with the Phone Oximeter, including one night simultaneously with PSG in the hospital and two nights at home. In this study, we aim to investigate the night to night variability and assess the accuracy of the oxygen desaturation index (ODI) screening for children with significant OSA. In order to assess the performance of the ODI calculation in children, we implemented different ODIs at different desaturation levels and time durations. The variability was studied using a one-way ANOVA, and ODI's performance screening for OSA using the area under the ROC curve (AUC). The implemented ODIs provide similar OSA screening results, using different apnea/hypopnea index (AHI) thresholds, as the ODI recommended for adults by the American academy of sleep medicine (AASM). The ODI provides an AUC of around 0.77, 0.76, 0.94 and 0.97 classifying children with an AHI > 1, AHI > 5 AHI > 10 and AHI > 15, respectively. The SMS dataset shows no significant night to night variability between the two nights at home. However, when comparing with the night at the hospital, both nights at home show a decrease in the lowest SpO2 value as well as overall SpO2 signal quality percentage. This study shows that there is variability in SpO2 signal between at-home versus in hospital settings.

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.

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.

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.

Assessment of respiratory flow cycle morphology in patients with chronic heart failure.

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

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.

Evaluation of a Novel Mobile Exergame in a School-Based Environment.

Physical inactivity is increasing among children globally and has been directly linked to the growing problems of overweight and obesity. We aim to assess the impact of a new mobile exergame, MobileKids Monster Manor (MKMM), in a school-based setting. MKMM, developed with input from youth to enhance physical activity, is wirelessly connected to an accelerometer-based activity monitor. Forty-two healthy students (11.3 ± 1.2 years old and 0.28 ± 1.29 body-mass index [BMI] z-score) participated in a randomized 4-week crossover study to evaluate the game intervention. The two study arms consisted of week-long baseline, game intervention/control, washout, and control/game intervention phases. All participants were required to wear an activity monitor at all times to record steps and active minutes for the study duration. MKMM was used during each arm's respective intervention week, during which children were asked to play the game at their convenience. When children were exposed to the game, an increase compared with the control phase of 2,934 steps per day (p = 0.0004, 95% CI 1,434-4,434) and 46 active minutes per day (p = 0.001, 95% CI 20-72) from baseline (12,299 steps/day and 190 active minutes/day) was observed. A linear regression model showed that MKMM yielded a greater increase in steps and active minutes per day among children with a higher BMI z-score, showing 10 percent more steps per day and 14 percent more active minutes per day relative to baseline, per unit increase in BMI z-score. In conclusion, MKMM increased steps and active minutes in a school-based environment. This suggests that mobile exergames could be useful tools for schools to promote physical activity and combat obesity in adolescents.

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.

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.

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.

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.

Estimation of respiratory rate from photoplethysmographic imaging videos compared to pulse oximetry.

We present a study evaluating two respiratory rate estimation algorithms using videos obtained from placing a finger on the camera lens of a mobile phone. The two algorithms, based on Smart Fusion and empirical mode decomposition (EMD), consist of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmographic signals to estimate respiratory rate. With custom-built software on an Android phone, photoplethysmographic imaging videos were recorded from 19 healthy adults while breathing spontaneously at respiratory rates between 6 to 32 breaths/min. Signals from two pulse oximeters were simultaneously recorded to compare the algorithms' performance using mobile phone data and clinical data. Capnometry was recorded to obtain reference respiratory rates. Two hundred seventy-two recordings were analyzed. The Smart Fusion algorithm reported 39 recordings with insufficient respiratory information from the photoplethysmographic imaging data. Of the 232 remaining recordings, a root mean square error (RMSE) of 6 breaths/min was obtained. The RMSE for the pulse oximeter data was lower at 2.3 breaths/min. RMSE for the EMD method was higher throughout all data sources as, unlike the Smart Fusion, the EMD method did not screen for inconsistent results. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates greater than 20 breaths/min, independent of data source or algorithm tested.

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.