Assessment of Signal Processing Methods for Measuring the Respiratory Rate in the Neonatal Intensive Care Unit

Methods for calculating ventilation rates during resuscitation from out-of-hospital cardiac arrest

OBJECTIVE

Ventilation control is important during resuscitation from out-of-hospital cardiac arrest (OHCA). We compared different methods for calculating ventilation rates (VR) during OHCA.

METHODS

We analyzed data from the Pragmatic Airway Resuscitation Trial, identifying ventilations through capnogram recordings. We determined VR by: 1) counting the number of breaths within a time epoch ("counted" VR), and 2) calculating the mean of the inverse of measured time between breaths within a time epoch ("measured" VR). We repeated the VR estimates using different time epochs (10, 20, 30, 60 sec). We defined hypo- and hyperventilation as VR <6 and >12 breaths/min, respectively. We assessed differences in estimated hypo- and hyperventilation with each VR measurement technique.

RESULTS

Of 3,004 patients, data were available for 1,010. With the counted method, total hypoventilation increased with longer time epochs ([10-s epoch: 75 sec hypoventilation] to [60-s epoch: 97 sec hypoventilation]). However, with the measured method, total hypoventilation decreased with longer time epochs ([10-s epoch: 223 sec hypoventilation] to [60-s epoch: 150 sec hypoventilation]). With the counted method, the total duration of hyperventilation decreased with longer time epochs ([10-s epochs: 35 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). With the measured method, total hyperventilation decreased with longer time epochs ([10-s epoch: 78 sec hyperventilation] to [60-s epoch: 0 sec hyperventilation]). Differences between the measured and counted estimates were smallest with a 60-s time epoch.

CONCLUSIONS

Quantifications of hypo- and hyperventilation vary with the applied measurement methods. Measurement methods are important when characterizing ventilation rates in OHCA.

Non-contact physiological monitoring of post-operative patients in the intensive care unit

Prolonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

New method to measure interbreath intervals in infants for the assessment of apnoea and respiration

Background

Respiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity.

Methods

We develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of breaths. The algorithm is refined by comparing its accuracy with clinically observed breaths and pauses in breathing. We develop an automated classifier to differentiate periods of true apnoea from artefactually low amplitude signal. We assess the performance of this algorithm in the detection of morphine-induced respiratory depression. Finally, we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution.

Results

Individual breaths were detected with a false-positive rate of 13% and a false-negative rate of 12%. The classifier identified true apnoeas with an accuracy of 93%. As expected, morphine caused a significant shift in the IBI distribution towards longer IBIs. Following ROP screening, there was a significant increase in pauses in breathing that lasted more than 10 s (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate and no episodes of apnoea were recorded in the medical records.

Conclusions

We show that our algorithm offers an improved method for the identification of IBIs and apnoeas in preterm infants. Following ROP screening, increased respiratory instability can occur even in the absence of clinically significant apnoeas. Accurate assessment of infant respiratory activity is essential to inform clinical practice.

An E-Textile Respiration Sensing System for NICU Monitoring: Design and Validation

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.

Respiratory synchrony comparison between preterm and full-term neonates using inertial sensors

INTRODUCTION

Due to inefficient respiratory control, newborns become prone to asynchronous thoracoabdominal (TA) movements. The present study quantitatively estimated the synchrony of TA in preterm and full-term newborns through an inertial and magnetic measurement units (IMMUs) system.

METHODS

This cross-sectional study was conducted with 20 newborns divided into Preterm Group (PTG, n = 10) and Full-Term Group (FTG, n = 10). Each neonate had IMMUs placed on the sternum and near the umbilicus, thus the TA motion was estimated through the resultant inclination angles calculated using a sensor fusion filter. The respiratory incursions were also manually counted and video-recorded for two minutes, then used to validate a Matlab custom-written routine for their automatic identification. The respiratory cycles were used to calculate the phase change angle (φ) between the thoracic and abdominal compartments. Association between the manual and automatic methods were verified by Pearson's correlation and root mean squared errors (RMSE), and the comparison between the groups was performed through the Student's t test with α = .05.

RESULTS

The values of respiratory incursions measured by both methods showed a high association and low measurement error (r = .96, RMSE = 9.8, p < .001). The FTG presented a higher occurrence of TA synchrony (p = .049) while the PTG group presented a higher occurrence of TA asynchrony (p = .036). No difference was found between the groups regarding the paradoxical classification (p = .071).

CONCLUSION

The proposed method was valid to quantitatively assess the TA synchrony of hospitalized neonates. Preterm infants had a higher occurrence of the asynchronous respiratory pattern in comparison to full-term infants.

Reliability of a computational model for evaluating thoracoabdominal mobility in newborns: a cross-sectional study

The present study aimed to verify the inter and intra-examiner reliability of an interactive custom-made MATLAB® App for bio-photogrammetric analysis of thoracoabdominal mobility in newborns and compare the respiratory rate (RR) results between the automatic MATLAB® App and its manual counterpart. This is a cross-sectional study conducted in 27 healthy newborns of both sexes (gestational age between 37 and 41 weeks and up to 72 h of life) who did not cry during data acquisition. Chest and abdominal areas of the subjects in the supine position were analyzed through 60 s videos, totaling 30,714 photograms. All photograms were analyzed by three examiners on three different occasions. Analysis of variance (ANOVA) and intraclass correlation coefficient (ICC) were applied, adopting a 95% confidence interval and significance level of α = 0.05. Reliability was excellent for intra (ICC 0.81-0.96) and inter-examiner correlations (ICC 0.84-0.99) between the chest and abdominal areas, in both inspiration and expiration, with no differences between them. Evaluation of newborns' thoracoabdominal mobility using the custom-made MATLAB® App for bio-photogrammetric analysis exhibited good to excellent intra- and inter-examiner reliability and an excellent correlation between manual and automatic models for measuring RR. Thus, it proved to be an objective and practical tool for bedside thoracoabdominal mobility assessment in different clinical situations involving neonatal care.

Non-contact breathing rate monitoring in newborns: A review

The neonatal period - the first 4 weeks of life - is the most critical time for a child's survival. Breathing rate is a vital indicator of the health condition and requires continuous monitoring in case of sickness or preterm birth. Breathing movements can be counted by contact and non-contact methods. In the case of newborn infants, the non-contact breathing rate monitoring need is high, as a contact-based approach may interfere while providing care and is subject to interference by non-breathing movements. This review article delivers a factual summary, and describes the methods and processing involved in non-contact based breathing rate monitoring. The article also provides the advantages, limitations, and clinical applications of these methods. Additionally, signal processing, feasibility, and future direction of different non-contact neonatal breathing rate monitoring are discussed.

Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.