Object Detection During Newborn Resuscitation Activities.

OBJECTIVE: Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; however, the importance and effect of the different treatments are not fully explored. The available data are collected in Tanzania, during newborn resuscitation, for analysis of the resuscitation activities and the response of the newborn. An important step in the analysis is to create activity timelines of the episodes, where activities include ventilation, suction, stimulation, etc. Methods: The available recordings are noisy real-world videos with large variations. We propose a two-step process in order to detect activities possibly overlapping in time. The first step is to detect and track the relevant objects, such as bag-mask resuscitator, heart rate sensors, etc., and the second step is to use this information to recognize the resuscitation activities. The topic of this paper is the first step, and the object detection and tracking are based on convolutional neural networks followed by post processing. RESULTS: The performance of the object detection during activities were 96.97% (ventilations), 100% (attaching/removing heart rate sensor), and 75% (suction) on a test set of 20 videos. The system also estimate the number of health care providers present with a performance of 71.16%. CONCLUSION: The proposed object detection and tracking system provides promising results in noisy newborn resuscitation videos. SIGNIFICANCE: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities.

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.