- Related item:
- J. Jorge, M. Villarroel, S. Chaichulee, A. Guazzi, G. Green, K. McCormick and L. Tarassenko.:Assessment of signal processing methods for measuring the respiratory rate in the neonatal intensive care unit, IEEE Journal of Biomedical and Health Informatics, January 2019
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Knowledge of the pathological instabilities in the breathing pattern can provide valuable insights into the cardiorespiratory status of the critically-ill infant as well as their maturation level. This paper is concerned with the measurement of respiratory rate (RR) in premature infants. We compare the rates estimated from (a) the chest impedance pneumogram, (b) the ECG-derived respiratory rhythms, and (c) the PPG-derived respiratory rhythms against those measured in the reference standard of breath detection provided by attending clinical staff during 165 manual breath counts. We demonstrate that accurate RR estimates can be produced from all sources for RR in the 40 - 80 bpm (breaths per min) range. We also conclude that the use of indirect methods based on the ECG or the PPG poses a fundamental challenge in this population due to their poor behaviour at fast breathing rates (upwards of 80 bpm).
- Related item:
- M. Villarroel, A. Guazzi, J. Jorge, S. Davis, P. Watkinson, G. Green, A. Shenvi, K. McCormick, L. Tarassenko.: Continuous non-contact vital sign monitoring in neonatal intensive care unit Healthcare Technology Letters 1(3):87-91, 2014
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Current technologies to allow continuous monitoring of vital signs in pre-term infants in the hospital require adhesive electrodes or sensors to be in direct contact with the patient. These can cause stress, pain, and also damage the fragile skin of the infants. It has been established previously that the colour and volume changes in superficial blood vessels during the cardiac cycle can be measured using a digital video camera and ambient light, making it possible to obtain estimates of heart rate or breathing rate. Most of the papers in the literature on non-contact vital sign monitoring report results on adult healthy human volunteers in controlled environments for short periods of time. The authors' current clinical study involves the continuous monitoring of pre-term infants, for at least four consecutive days each, in the high-dependency care area of the Neonatal Intensive Care Unit (NICU) at the John Radcliffe Hospital in Oxford. The authors have further developed their video-based, non-contact monitoring methods to obtain continuous estimates of heart rate, respiratory rate and oxygen saturation for infants nursed in incubators. In this Letter, it is shown that continuous estimates of these three parameters can be computed with an accuracy which is clinically useful. During stable sections with minimal infant motion, the mean absolute error between the camera-derived estimates of heart rate and the reference value derived from the ECG is similar to the mean absolute error between the ECG-derived value and the heart rate value from a pulse oximeter. Continuous non-contact vital sign monitoring in the NICU using ambient light is feasible, and the authors have shown that clinically important events such as a bradycardia accompanied by a major desaturation can be identified with their algorithms for processing the video signal.
- Related item:
- João Jorge, Mauricio Villarroel, Sitthichok Chaichulee, Kenny McCormick, Lionel Tarassenko, "Data fusion for improved camera-based detection of respiration in neonates," Proc. SPIE 10501, Optical Diagnostics and Sensing XVIII: Toward Point-of-Care Diagnostics, 1050112
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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.
- Related item:
- J. Jorge, M. Villarroel, S. Chaichulee, A. Guazzi, S. Davies, G. Green, K. McCormick and L. Tarassenko.: Non-Contact Monitoring of Respiration in the Neonatal Intensive Care Unit 12th IEEE International Conference on Automatic Face & Gesture Recognition, 2017
- Description:
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An abnormal respiratory rhythm is an early
indicator of physiological deterioration. It is of critical importance in the clinical management of critically-ill or premature infants, for whom apnoea of prematurity is a major concern. Nevertheless, respiratory signals are still largely disregarded in neonatal intensive care units due to the high prevalence of noise and high false alarm rates in conventional monitoring.
To address this, we present a novel method for the extraction of respiration from camera-based measurements taken from the top-view of an incubator. A total of 107 events from 30 neonatal admissions were annotated by three clinical reviewers as either true cessations of breathing (physiologically relevant) or false (artefact-related). The events were divided into two independent groups for training and validation and our algorithm was trained to classify true cessations. We achieved a good classification performance with 9 out of 10 cessations and 7 out of 10 artefactual events correctly identified in the training set, and with 7 out of 10 cessations and 34 out of 44 artefactual events correctly identified in the out-of-sample test set. A reduction in false alarm rate of 77.3% was achieved.
