Automatic Detection and Parameterization of Manual Bag-Mask Ventilation on Newborns.

OBJECTIVES

Birth asphyxia is a condition where a fetus suffers from lack of oxygen during birth. Intervention by manual ventilation should start within one minute after birth. Bag-mask resuscitators are commonly used in situations where ventilation is provided by a single health care worker. Due to a high complexity of interactions between physiological conditions of the newborns and the clinical treatment, the recommendations for bag-mask ventilation of infants remains controversial. The purpose of this paper is to illustrate the processing and parameterization of ventilation signals recorded from the Laerdal newborn resuscitation monitor into meaningful data.

METHODS

Basic signal processing approaches are applied on various signal channels (airway pressure, flow, CO 2, and ECG) to detect events related to ventilation activities.

RESULTS

Different types of events are detected and parameterized to describe the characteristics of ventilation procedure.

CONCLUSIONS

Efficient detection algorithms as well as parameterization of ventilation events could be useful for retrospective analysis of resuscitation data, for example, by finding the association between different ventilation parameters and positive responses of newborns.

SIGNIFICANCE: Information about ventilation events and ventilation parameters could potentially be useful during a resuscitation situation by giving immediate feedback to the health care provider.