Multichannel Esophageal Heart Rate Monitoring of Preterm Infants

Diaphragmatic electromyography in infants: an overview of possible clinical applications

Preterm infants often experience breathing instability and a hampered lung function. Therefore, these infants receive cardiorespiratory monitoring and respiratory support. However, the current respiratory monitoring technique may be unreliable for especially obstructive apnea detection and classification and it does not provide insight in breathing effort. The latter makes the selection of the adequate mode and level of respiratory support difficult. Electromyography of the diaphragm (dEMG) has the potential of monitoring heart rate (HR) and respiratory rate (RR), and it provides additional information on breathing effort. This review summarizes the available evidence on the clinical potential of dEMG to provide cardiorespiratory monitoring, to synchronize patient-ventilator interaction, and to optimize the mode and level of respiratory support in the individual newborn infant. We also try to identify gaps in knowledge and future developments needed to ensure widespread implementation in clinical practice. IMPACT: Preterm infants require cardiorespiratory monitoring and respiratory support due to breathing instability and a hampered lung function. The current respiratory monitoring technique may provide unreliable measurements and does not provide insight in breathing effort, which makes the selection of the optimal respiratory support settings difficult. Measuring diaphragm activity could improve cardiorespiratory monitoring by providing insight in breathing effort and could potentially have an important role in individualizing respiratory support in newborn infants.

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.

Multichannel esophageal signals to monitor respiratory rate in preterm infants

BACKGROUND

Apnea of prematurity cannot be reliably measured with current monitoring techniques. Instead, indirect parameters such as oxygen desaturation or bradycardia are captured. We propose a Kalman filter-based detection of respiration activity and hence apnea using multichannel esophageal signals in neonatal intensive care unit patients.

METHODS

We performed a single-center observational study with moderately preterm infants. Commercially available nasogastric feeding tubes containing multiple electrodes were used to capture signals with customized software. Multichannel esophageal raw signals were manually annotated, processed using extended Kalman filter, and compared with standard monitoring data including chest impedance to measure respiration activity.

RESULTS

Out of a total of 405.4 h captured signals in 13 infants, 100 episodes of drop in oxygen saturation or heart rate were examined. Median (interquartile range) difference in respiratory rate was 0.04 (-2.45 to 1.48)/min between esophageal measurements annotated manually and with Kalman filter and -3.51 (-7.05 to -1.33)/min when compared to standard monitoring, suggesting an underestimation of respiratory rate when using the latter.

CONCLUSIONS

Kalman filter-based estimation of respiratory activity using multichannel esophageal signals is safe and feasible and results in respiratory rate closer to visual annotation than that derived from chest impedance of standard monitoring.