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

Khanam, Fatema-Tuz-Zohra; Perera, Asanka G.; Al-Naji, Ali; Gibson, Kim; Chahl, Javaan

doi:10.3390/jimaging7080122

Cited by 36 publications

(20 citation statements)

References 69 publications

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“…Therefore, it is robust no matter what movement the babies make. Another advantage is that it is fast and convenient compared with training a neonatal face classifier [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

et al. 2021

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Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement. Methods This paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements. Results Experimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. Conclusions To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.

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“…Therefore, it is robust no matter what movement the babies make. Another advantage is that it is fast and convenient compared with training a neonatal face classifier [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

et al. 2021

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“…Similarly to adult-based existing work, the majority of past works in the neonatal space have focused on deep learning-based methods [1]- [3], [9]. In particular, the YOLO framework has been especially popular [1], [2], [9].…”

Section: B Neonatal Face and Facial Landmarks Detectionmentioning

confidence: 99%

Neonatal Face and Facial Landmark Detection from Video Recordings

Grooby¹,

Sitaula²,

Ahani³

et al. 2023

Preprint

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This paper explores automated face and facial landmark detection of neonates, which is an important first step in many video-based neonatal health applications, such as vital sign estimation, pain assessment, sleep-wake classification, and jaundice detection. Utilising three publicly available datasets of neonates in the clinical environment, 366 images (258 subjects) and 89 (66 subjects) were annotated for training and testing, respectively. Transfer learning was applied to two YOLO-based models, with input training images augmented with random horizontal flipping, photo-metric colour distortion, translation and scaling during each training epoch. Additionally, the re-orientation of input images and fusion of trained deep learning models was explored. Our proposed model based on YOLOv7Face outperformed existing methods with a mean average precision of 84.8% for face detection, and a normalised mean error of 0.072 for facial landmark detection. Overall, this will assist in the development of fully automated neonatal health assessment algorithms.Clinical relevance-Accurate face and facial landmark detection provides an automated and non-contact option to assist in video-based neonatal health applications.

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“…However, it has been suggested that more research is needed for dark-skinned subjects and dark ambient conditions. Further, Khanam et al [21] used a CNN for the ROI selection and noise-assisted signal decomposition to supress the noise in the extracted respiratory signal.…”

Section: Related Workmentioning

confidence: 99%

Non-contact respiratory rate monitoring using thermal and visible imaging: a pilot study on neonates

Maurya

Zwiggelaar

Chawla

et al. 2022

J Clin Monit Comput

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Respiratory rate (RR) monitoring is essential in neonatal intensive care units. Despite its importance, RR is still monitored intermittently by manual counting instead of continuous monitoring due to the risk of skin damage with prolonged use of contact electrodes in preterm neonates and false signals due to displacement of electrodes. Thermal imaging has recently gained significance as a non-contact method for RR detection because of its many advantages. However, due to the lack of information in thermal images, the selection and tracking of the region of interest (ROI) in thermal images for neonates are challenging. This paper presents the integration of visible (RGB) and thermal (T) image sequences for the selection and tracking of ROI for breathing rate extraction. The deep-learning based tracking-by-detection approach is employed to detect the ROI in the RGB images, and it is mapped to the thermal images using the RGB-T image registration. The mapped ROI in thermal spectrum sequences gives the respiratory rate. The study was conducted first on healthy adults in different modes, including steady, motion, talking, and variable respiratory order. Subsequently, the method is tested on neonates in a clinical settings. The findings have been validated with a contact-based reference method.The average absolute error between the proposed and belt-based contact method in healthy adults reached 0.1 bpm and for more challenging conditions was approximately 1.5 bpm and 1.8 bpm, respectively. In the case of neonates, the average error is 1.5 bpm, which are promising results. The Bland–Altman analysis showed a good agreement of estimated RR with the reference method RR and this pilot study provided the evidence of using the proposed approach as a contactless method for the respiratory rate detection of neonates in clinical settings.

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

Cited by 36 publications

References 69 publications

Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts

Neonatal Face and Facial Landmark Detection from Video Recordings

Non-contact respiratory rate monitoring using thermal and visible imaging: a pilot study on neonates

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