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Article

Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks

1
UniSA STEM, Mawson Lakes Campus, University of South Australia, Mawson Lakes, SA 5095, Australia
2
Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, Iraq
3
Clinical and Health Sciences, City East Campus, University of South Australia, North Terrace, Adelaide, SA 5000, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Raimondo Schettini
J. Imaging 2021, 7(8), 122; https://doi.org/10.3390/jimaging7080122
Received: 5 May 2021 / Revised: 18 July 2021 / Accepted: 19 July 2021 / Published: 23 July 2021
(This article belongs to the Section Image and Video Processing)
Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent decades. However, studies on infants in the Neonatal Intensive Care Unit (NICU) are still limited. Therefore, we conducted a single-center study to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants in NICU using a digital camera. The region of interest (ROI) was automatically selected using a convolutional neural network and signal decomposition was used to minimize the noise artefacts. The experimental results have been validated with the reference data obtained from an ECG monitor. They showed a strong correlation using the Pearson correlation coefficients (PCC) of 0.9864 and 0.9453 for HR and RR, respectively, and a lower error rate with RMSE 2.23 beats/min and 2.69 breaths/min between measured data and reference data. A Bland–Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR. Therefore, this technique may be applicable in clinical environments as an economical, non-contact, and easily deployable monitoring system, and it also represents a potential application in home health monitoring. View Full-Text
Keywords: heart rate; respiratory rate; NICU; convolutional neural network; signal decomposition heart rate; respiratory rate; NICU; convolutional neural network; signal decomposition
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MDPI and ACS Style

Khanam, F.-T.-Z.; Perera, A.G.; Al-Naji, A.; Gibson, K.; Chahl, J. Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks. J. Imaging 2021, 7, 122. https://doi.org/10.3390/jimaging7080122

AMA Style

Khanam F-T-Z, Perera AG, Al-Naji A, Gibson K, Chahl J. Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks. Journal of Imaging. 2021; 7(8):122. https://doi.org/10.3390/jimaging7080122

Chicago/Turabian Style

Khanam, Fatema-Tuz-Zohra, Asanka G. Perera, Ali Al-Naji, Kim Gibson, and Javaan Chahl. 2021. "Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks" Journal of Imaging 7, no. 8: 122. https://doi.org/10.3390/jimaging7080122

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