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International audience; In very preterm infants, cardio-respiratory events and associated hypoxemia occurring during early postnatal life have been associated with risks of retinopathy, growth alteration and neurodevelopment impairment. These events are commonly detected by continuous cardio-respiratory monitoring in neonatal intensive care units (NICU), through the associated bradycardia. NICU nurse interventions are mainly triggered by these alarms. In this work, we acquired data from 52 preterm infants during NICU monitoring, in order to propose an early bradycardia detector which is based on a decentralized fusion of three detectors. The main objective is to improve automatic detection under real-life conditions without altering performance with respect to that of a monitor commonly used in NICU. We used heart rate lower than 80 bpm during at least 10 sec to define bradycardia. With this definition we observed a high rate of false alarms (64%) in real-life and that 29% of the relevant alarms were not followed by manual interventions. Concerning the proposed detection method, when compared to current monitors, it provided a significant decrease of the detection delay of 2.9 seconds, without alteration of the sensitivity (97.6% vs 95.2%) and false alarm rate (63.7% vs 64.1%). We expect that such an early detection will improve the response of the newborn to the intervention and allow for the development of new automatic therapeutic strategies which could complement manual intervention and decrease the sepsis risk. more...

Camera-based infant monitoring has received substantial attention because of its unobtrusiveness, allowing long-term and continuous monitoring. To assess infant sleep, it is required to know whether the infant is in bed or out of bed. This can automate monitoring for analyzing data solely when the infant is in bed, providing accurate assessment of sleep quality. In this work, we propose a method aimed at detecting infant in/out of bed status for a 24 h period using motion estimates derived from an infrared video camera. The method is based on several heuristic decision-making rules to examine the motions between peaky motions that are expected to express activities or events of putting the infant in bed or taking the infant out of bed. For example, when these events are identified, intuitively, if a period between two events has consistently motions (caused by infant's body movements), this period should be detected as infant 'in bed', otherwise as 'out of bed'. Our proposed method would largely increase the efficiency (i.e. reduce the computational load) compared to some advanced computer vision algorithms that try to recognize infant's face or torso. To separate the motions caused by infant body movements and other activities from, for example, parents, a region of interest (ROI) is configured. This ROI can be fixed and customized. 77 datasets of 24 h recordings from five infant participants were analyzed in this study. Experiments were conducted in a free-living environment in the participants' own home. Results show that an average accuracy of 95.8% using a fixed ROI configuration, and that of 96.9% using a customized configuration were achieved. This indicates that our proposed method is reliable for infant in/out of bed detection. more...

Prolonged monitoring of infant sleep is paramount for parents and healthcare professionals for interpreting and evaluating infants&rsquo, sleep quality. Wake-sleep patterns are often studied to assess this. Video cameras have received a lot of attention in infant sleep monitoring because they are unobtrusive and easy to use at home. In this paper, we propose a method using motion data detected from infrared video frames (video-based actigraphy) to identify wake and sleep states. The motion, mostly caused by infant body movement, is known to be substantially associated with infant wake and sleep states. Two features were calculated from the video-based actigraphy, and a Bayesian-based linear discriminant classification model was employed to classify the two states. Leave-one-subject-out cross validation was performed to validate our proposed wake and sleep classification model. From a total of 11.6 h of infrared video recordings of 10 healthy term infants in a laboratory pilot study, we achieved a reliable classification performance with a Cohen&rsquo, s kappa coefficient of 0.733 &plusmn, 0.204 (mean &plusmn, standard deviation) and an overall accuracy of 92.0% &plusmn, 4.6%. more...

The sick preterm infant monitoring is an intriguing job that medical staff in Neonatal Intensive Care Units (NICU) must deal with on a daily basis. As a standards monitoring procedure, preterm infants are monitored via sensors and electrodes that are firmly attached to their fragile and delicate skin and connected to processing monitors. However, an alternative exists in contactless imaging to record such physiological signals (we call it as Physio-Markers), detecting superficial changes and internal structures activities which can be used independently of, or aligned with, conventional monitors. Countless advantages can be gained from unobtrusive monitoring not limited to: (1) quick data generation; (2) decreasing physical and direct contact with skin, which reduces skin breakdown and minimizes risk of infection; and (3) reduction of electrodes and probes connected to clinical monitors and attached to the skin, which allows greater body surface-area for better care. This review is an attempt to build a solid ground for and to provide a clear perspective of the potential clinical applications of technologies inside NICUs that use contactless imaging modalities such as Visible Light Imaging (VLI), Near Infrared Spectroscopy (NIRS), and Infrared Thermography (IRT). more...