NRP: A Multi-Source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units

Abstract

The incidence of premature births continues to rise each year and premature babies require specialized care in neonatal intensive care units (NICUs). The intermittent monitoring of these babies, however, poses challenges for clinicians in terms of visualizing and detecting meaningful diagnostic trends. In order to address this issue, the adoption of continuous, multi-parameter, electronic medical record-keeping methods is promising in terms of leveraging advanced analytics techniques and potentially improving health outcomes. In this article, we present the design and implementation of the Neonates Recording Platform (NRP), a hardware-software tool to be deployed at the bedside in a real NICU environment. The NRP enables data from various sources to be collected, labelled, processed and stored. We conducted tests involving the acquisition of neonates’ physiological parameters, synchronized with video recordings, in addition to real-time analysis of body pose, with the capture of up to 33 reference points, and audio files from both the infant and the environment. In NRP, the collected data is organized hierarchically in a portable format and is automatically cleaned and validated, thereby ensuring its usability for healthcare professionals and data scientists. Additionally, NRP enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the NRP platform is the integration of a unique computer vision algorithm called CardMed, which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of NRP involved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain.