Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study

Gibson, Kim; Al-Naji, Ali; Fleet, Julie-Anne; Steen, Mary; Esterman, Adrian; Chahl, Javaan; Huynh, Jasmine; Morris, Scott

doi:10.1038/s41390-019-0506-5

Cited by 33 publications

(25 citation statements)

References 27 publications

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“…The results are comparable to the findings of recent and relevant publications, such as [9] presenting the limits of agreement were -22 to 23.6 BPM on a smaller database. Two Fig.…”

Section: A Nicu Experimentssupporting

confidence: 90%

“…blind source separation [2], [3], optical flow [4] for respiration-signal and Fourier analysis for rate value estimation [5], [6]). Recent studies relies on either mixed [7], [8] or neural network solutions [9]- [11] for these tasks to increase motion robustness and overall performance. Another interesting approach is to exploit local motion magnification algorithm [12], which in principle relies on regular, periodic motion enhancement and thus the very irregular respiration of the newborn infants is hard to handle with.…”

Section: Introductionmentioning

confidence: 99%

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Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

2021

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The article describes a reference and training set free incrementally trained deep learning algorithm for camera-based respiration monitoring systems. The algorithm uses a model based discriminator to find salient areas having respiration like periodic motion. It stores the first principle component of the found waveforms into two slowly growing set along with negative, uncorrelated motion patterns. Using these samples, it trains a deep neural network classifier incrementally to recognize respiration from sudden and motion intensive situations. The classifier had no forgetting mechanism and it is able to adapt quickly the changing respiration patterns and conditions. The algorithm has been validated in a total of 24 hours diverse recording captured in the neonatal intensive care unit (NICU) of the I

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“…The results are comparable to the findings of recent and relevant publications, such as [9] presenting the limits of agreement were -22 to 23.6 BPM on a smaller database. Two Fig.…”

Section: A Nicu Experimentssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

2021

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“…Study enrolment and data collection began in May 2018. Results of this study have recently been published [23].…”

Section: Resultsmentioning

confidence: 99%

Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study

Gibson¹,

Al-Naji²,

Fleet³

et al. 2019

JMIR Res Protoc

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BackgroundBiomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit.ObjectiveThe aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit.MethodsA single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time.ResultsStudy enrolment began in May 2018. Results of this study were published in July 2019.ConclusionsThe study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications.International Registered Report Identifier (IRRID)RR1-10.2196/13400

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“…Another useful index to classify sleep and wake states is respiration information 28,29 . Respiration information can be obtained from video without contact 30,31 . Sensitivity and specificity can also be improved by applying a nonlinear classification method such as machine learning 32 .…”

Section: Discussionmentioning

confidence: 99%

Video‐based neonatal state assessment method for timing of procedures

Okamoto

Imai

et al. 2021

Pediatrics International

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Background Procedures should be performed when an infant is most receptive to disruptions in order to reduce the stress on the infant. However, frequent direct observations place a heavy burden on medical staff. There is therefore a need for a method for quantitatively and automatically evaluating the neonatal state. Methods Ten infants in our hospital were enrolled in this study. The states of the infants were assessed by medical staff using the Brazelton Neonatal Behavioral Assessment Scale and were recorded on video at the same time. The recorded states were reclassified as activity levels, a new state classification method that includes middle activity, which is the appropriate time for a procedure. Using image analysis, motions of the infant were quantified as two indices: activity and pause time. Activity and pause time were compared for each activity level. The cutoff values of the indices were calculated, and the sensitivity and specificity of the middle activity were calculated. Results There was a significant difference between all groups of activity level (P < 0.01). The maximum sensitivity and specificity of middle activity were 71.7% and 51.2%, respectively. Conclusions The neonatal state of infants can be quantitatively and automatically evaluated using video cameras, and the activity level can be used to determine an appropriate time for procedures in infants. This will reduce the burden on medical staff and lead to less stressful procedures for infants.

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Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study

Cited by 33 publications

References 27 publications

Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

Reference Free Incremental Deep Learning Model Applied for Camera-Based Respiration Monitoring

Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study

Video‐based neonatal state assessment method for timing of procedures

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