Non-contact vital-sign estimation allows the monitoring of physiological parameters (such as heart rate, respiratory rate, and peripheral oxygen saturation) without contact electrodes or sensors. Our recent work has demonstrated that a convolutional neural network (CNN) can be used to detect the presence of a patient and segment the patient’s skin area for vital-sign estimation, thus enabling the automatic continuous monitoring of vital signs in a hospital environment.
In a study approved by the local Research Ethical Committee, we made video recordings of pre-term infants nursed in a Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital in Oxford, UK. We extended the CNN model to detect the head, torso and diaper of the infants. We extracted multiple photoplethysmographic imaging (PPGi) signals from each body part, analysed their signal quality, and compared them with the PPGi signal derived from the entire skin area. Our results demonstrated the benefits of estimating heart rate combined from multiple regions of interest using data fusion. In the test dataset, we achieved a mean absolute error of 2.4 beats per minute for 80% (31.1 hours) from a total recording time of 38.5 hours for which both reference heart rate and video data were valid.
Monitoring respiration during neonatal sleep is notoriously difficult due to the nonstationary nature of the signals and the presence of spurious noise. Current approaches rely on the use of adhesive sensors, which can damage the fragile skin of premature infants. Recently, non-contact methods using low-cost RGB cameras have been proposed to acquire this vital sign from (a) motion or (b) photoplethysmographic signals extracted from the video recordings. Recent developments in deep learning have yielded robust methods for subject detection in video data. In the analysis described here, we present a novel technique for combining respiratory information from high-level visual descriptors provided by a multi-task convolutional neural network. Using blind source separation, we find the combination of signals which best suppresses pulse and motion distortions and subsequently use this to extract a respiratory signal. Evaluation results were obtained from recordings on 5 neonatal patients nursed in the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital, Oxford, UK. We compared respiratory rates derived from this fused breathing signal against those measured using the gold standard provided by the attending clinical staff. We show that respiratory rate (RR) be accurately estimated over the entire range of respiratory frequencies.
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