Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks
Article
Article Title | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
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ERA Journal ID | 213284 |
Article Category | Article |
Authors | Khanam, Fatema-Tuz-Zohra, Perera, Asanka G., Al-Naji, Ali, Gibson, Kim and Chahl, Javaan |
Journal Title | Journal of Imaging |
Journal Citation | 7 (8) |
Article Number | 122 |
Number of Pages | 19 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2313-433X |
Digital Object Identifier (DOI) | https://doi.org/10.3390/jimaging7080122 |
Web Address (URL) | https://www.mdpi.com/2313-433X/7/8/122 |
Abstract | 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. |
Keywords | heart rate; respiratory rate; NICU; convolutional neural network; signal decomposition |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
Byline Affiliations | University of South Australia |
School of Engineering | |
Middle East Technical University, Turkey |
https://research.usq.edu.au/item/z77x4/non-contact-automatic-vital-signs-monitoring-of-infants-in-a-neonatal-intensive-care-unit-based-on-neural-networks
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