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Neonatal non-contact respiratory monitoring based on real-time infrared thermography.

ABSTRACT: BACKGROUND: Monitoring of vital parameters is an important topic in neonatal daily care. Progress in computational intelligence and medical sensors has facilitated the development of smart bedside monitors that can integrate multiple parameters into a single monitoring system. This paper describes non-contact monitoring of neonatal vital signals based on infrared thermography as a new biomedical engineering application. One signal of clinical interest is the spontaneous respiration rate of the neonate. It will be shown that the respiration rate of neonates can be monitored based on analysis of the anterior naris (nostrils) temperature profile associated with the inspiration and expiration phases successively. OBJECTIVE: The aim of this study is to develop and investigate a new non-contact respiration monitoring modality for neonatal intensive care unit (NICU) using infrared thermography imaging. This development includes subsequent image processing (region of interest (ROI) detection) and optimization. Moreover, it includes further optimization of this non-contact respiration monitoring to be considered as physiological measurement inside NICU wards. RESULTS: Continuous wavelet transformation based on Debauches wavelet function was applied to detect the breathing signal within an image stream. Respiration was successfully monitored based on a 0.3°C to 0.5°C temperature difference between the inspiration and expiration phases. CONCLUSIONS: Although this method has been applied to adults before, this is the first time it was used in a newborn infant population inside the neonatal intensive care unit (NICU). The promising results suggest to include this technology into advanced NICU monitors.

SUBMITTER: Abbas AK

PROVIDER: S-EPMC3258209 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Neonatal non-contact respiratory monitoring based on real-time infrared thermography.

Abbas Abbas K AK Heimann Konrad K Jergus Katrin K Orlikowsky Thorsten T Leonhardt Steffen S

Biomedical engineering online 20111020

<h4>Background</h4>Monitoring of vital parameters is an important topic in neonatal daily care. Progress in computational intelligence and medical sensors has facilitated the development of smart bedside monitors that can integrate multiple parameters into a single monitoring system. This paper describes non-contact monitoring of neonatal vital signals based on infrared thermography as a new biomedical engineering application. One signal of clinical interest is the spontaneous respiration rate o ...[more]

PMID: 22243660

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Project description:Hypothermia is risk factor for piglet neonatal mortality, especially for low birth weight piglets. Piglets with intrauterine growth retardation (IUGR) also have a higher mortality risk at birth. This study aimed to validate infrared thermography (IRT) as an alternative to rectal temperature (RT) to measure piglet temperature in the hour postpartum, and to identify piglets with thermoregulation difficulties. At birth (6.3 ± 0.35 min postpartum), 67 piglets were dried, weighed, scored for growth retardation (IUGR; 0-3), and isolated in a plastic box where IRT images were taken, followed by RT. Piglets were then returned to the farrowing pen, and the process repeated at 15, 30, and 60 min postpartum. Piglets were ranked according to their weight (quartiles: 0.57-1.27 kg, 1.27-1.5 kg, 1.5-1.74 kg, 1.74-2.44 kg). Temperatures (ear base and tip; minimum, maximum and average of back) were extracted from IRT images (Thermacam Researcher Pro 2.0). Pearson correlations between temperature measures were calculated, and the effect of time, IUGR score, and weight were included in linear mixed models (SAS 9.4). RT was correlated with all IRT data across time points (P < 0.05); correlations were strongest with the ear base, and weakest with the ear tip and minimum back temperature. Both IUGR score and weight rank affected ear base (P < 0.05) and RTs (P < 0.05). The lightest piglets, and piglets with severe IUGR had the lowest temperature, relative to their counterparts. Indeed, differences between all weights categories were significant for RT. Piglets with the lowest weight (0.27-1.27 kg) had lower ear base temperatures than piglets in the third quartile (1.5-1.74 kg; 35.2 ± 0.36 °C vs. 36.5 ± 0.35 °C, t 64.9 = -4.51, P < 0.001) and the heaviest piglets (1.74-2.44 kg; 35.2 ± 0.36 °C vs. 36.4 ± 0.36 °C, t 70.4 = -3.97, P < 0.005). Overall, piglets with severe IUGR (score 3) had a lower RT than normal piglets (score 0; 35.8 ± 0.46 °C vs. 37.2 ± 0.42 °C, t 43.1 = 3.16, P < 0.05) and piglets with mild IUGR (score 1; 35.8 ± 0.46 °C vs. 37.1 ± 0.40 °C, t 45.3 = 2.92, P < 0.05); and they also had lower temperature at the base of the ear than normal piglets (35.1 ± 0.42 °C vs. 36.3 ± 0.36 °C, t 63.1 = 3.01, P < 0.05). These results confirmed that IRT is an interesting noninvasive tool for assessing neonatal piglets' thermoregulatory abilities and could be used in research investigating successful interventions for piglets at risk of hypothermia.

Project description:Background:Cellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach. Objectives:The purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy. Materials and methods:In this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22. Results:A combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%. Conclusions:The implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM).

Dataset Information

Neonatal non-contact respiratory monitoring based on real-time infrared thermography.

Publications

Neonatal non-contact respiratory monitoring based on real-time infrared thermography.

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