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

Article


Khanam, Fatema-Tuz-Zohra, Perera, Asanka G., Al-Naji, Ali, Gibson, Kim and Chahl, Javaan. 2021. "Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks." Journal of Imaging. 7 (8). https://doi.org/10.3390/jimaging7080122
Article Title

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

ERA Journal ID213284
Article CategoryArticle
AuthorsKhanam, Fatema-Tuz-Zohra, Perera, Asanka G., Al-Naji, Ali, Gibson, Kim and Chahl, Javaan
Journal TitleJournal of Imaging
Journal Citation7 (8)
Article Number122
Number of Pages19
Year2021
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2313-433X
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.

Keywordsheart rate; respiratory rate; NICU; convolutional neural network; signal decomposition
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Byline AffiliationsUniversity of South Australia
School of Engineering
Middle East Technical University, Turkey
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