Abstract
BackgroundHeart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement.MethodsThis paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements.ResultsExperimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG.ConclusionsTo the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.
Highlights
Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant
Contact HR measurement methods, such as electrocardiography measured by electrocardiogram (ECG) electrodes [4] and photoplethysmography (PPG) measured by pulse oximeters [5], have inherent limitations
Dopplers and infrared cameras are more expensive compared with commercial RGB cameras, whereas the white noise solution is unsuitable for long-term (e.g., 24 h) monitoring due to the annoying sounds it produces
Summary
Limitations and further improvements As the first study of improving robustness for HR measurement in a hospital, it still has some perspectives that can be enhanced in the future. When babies have unexpected head translation movements, the ROI dividing step introduces invalid background noise into the ROI, which leads to performance degradation during HR measurement This issue can be improved in future studies by adaptively switching between multiple cameras or calibrating facial orientations. This paper is based on the assumption that no large objects (which are close to skin color) exist in the video recordings If these kinds of objects exist in the video recordings, the morphology-based filtering cannot filter it out, which may bring background noise into pure HR signal and increase the HR measurement error. In the future, this issue can be improved by employing advanced techniques for distinguishing between elliptical faces and irregular shapes (such as nipples). Robustly estimating more parameters using different cameras in real-life situations will be taken into account
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