Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks
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 N...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/7/8/122 |
| _version_ | 1797523348419248128 |
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| author | Fatema-Tuz-Zohra Khanam Asanka G. Perera Ali Al-Naji Kim Gibson Javaan Chahl |
| author_facet | Fatema-Tuz-Zohra Khanam Asanka G. Perera Ali Al-Naji Kim Gibson Javaan Chahl |
| author_sort | Fatema-Tuz-Zohra Khanam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:41:40Z |
| format | Article |
| id | doaj.art-23b0458bf4c742e8b33637aad2d8ae21 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T08:41:40Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-23b0458bf4c742e8b33637aad2d8ae212023-11-22T08:13:42ZengMDPI AGJournal of Imaging2313-433X2021-07-017812210.3390/jimaging7080122Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural NetworksFatema-Tuz-Zohra Khanam0Asanka G. Perera1Ali Al-Naji2Kim Gibson3Javaan Chahl4UniSA STEM, Mawson Lakes Campus, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, Mawson Lakes Campus, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, Mawson Lakes Campus, University of South Australia, Mawson Lakes, SA 5095, AustraliaClinical and Health Sciences, City East Campus, University of South Australia, North Terrace, Adelaide, SA 5000, AustraliaUniSA STEM, Mawson Lakes Campus, University of South Australia, Mawson Lakes, SA 5095, AustraliaInfants 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.https://www.mdpi.com/2313-433X/7/8/122heart raterespiratory rateNICUconvolutional neural networksignal decomposition |
| spellingShingle | Fatema-Tuz-Zohra Khanam Asanka G. Perera Ali Al-Naji Kim Gibson Javaan Chahl Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks Journal of Imaging heart rate respiratory rate NICU convolutional neural network signal decomposition |
| title | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
| title_full | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
| title_fullStr | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
| title_full_unstemmed | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
| title_short | Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks |
| title_sort | non contact automatic vital signs monitoring of infants in a neonatal intensive care unit based on neural networks |
| topic | heart rate respiratory rate NICU convolutional neural network signal decomposition |
| url | https://www.mdpi.com/2313-433X/7/8/122 |
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