20 February 2018 Localised photoplethysmography imaging for heart rate estimation of pre-term infants in the clinic
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Abstract
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.
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Sitthichok Chaichulee, Mauricio Villarroel, João Jorge, Carlos Arteta, Gabrielle Green, Kenny McCormick, Andrew Zisserman, Lionel Tarassenko, "Localised photoplethysmography imaging for heart rate estimation of pre-term infants in the clinic", Proc. SPIE 10501, Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, 105010R (20 February 2018); doi: 10.1117/12.2289759; https://doi.org/10.1117/12.2289759
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