# Continuous Camera-Based Premature-Infant Monitoring Algorithms for NICU
## 7 Conclusions  
Algorithms for a single-camera-based premature-infant monitoring system was built and tested on videos recorded in a NICU. Beyond the standard pulse and breathing rate monitoring, the algorithms can measure the activity of the infants and can make logs of the nurse/parent activity as well. Our algorithms can operate and provide meaningful data continuously, using our Top-Level Classification Block, even when there is intensive infant movement happening or care is being performed in the incubator.  
A deep-learning-based solution was introduced for pulse detection, which was trained directly on pulse-rate data with SNRLoss objective function, rather than pulse curves. For respiration monitoring, a new heterogeneous algorithm was proposed. The system can operate with a single-color camera in daylight and can function in darkness with a near-infrared illumination.  
Frequency augmentation and video manipulation techniques were introduced to synthesize data for rare events such as very high/low pulse rate or apnea. Thus, we could train our networks to accurately handle such infrequent but very important situations and achieve better generalization.  
We achieved results regarding pulse rate similar to the ones measured for adults [19] implying that similar performance can be achieved in the case of infants even though their pulse is weaker and faster.  
Our respiration RMSE values are larger than the ones published in [5]. However, the authors of that paper used videos of younger, therefore less active infants and they used a dedicated camera for the respiration, placed in an optimal location for respiration monitoring, but unsuitable for pulse monitoring. When the two algorithms were compared on the same data sets our algorithm performed better.  
The execution times of the algorithms show the feasibility of the real-time application of our system on a relatively low-price PC or an embedded system with a GPU.  
Conceptualization, A.N., P.F., I.J., D.T., M.S. ([PERSON]), M.S. ([PERSON]), J.V. and A.Z.; methodology, software, validation and formal analysis, A.N., P.F., I.J., D.T., M.S.; investigation, A.N., I.J., M.S.; resources, M.S. ([PERSON]), J.V.; data curation, P.F., I.J.; writing--original draft preparation, A.N., P.F., I.J., D.T., M.S. ([PERSON]), A.Z.; writing--review and editing, A.N., I.J., A.Z.; visualization, A.N., P.F., D.T., I.J.; supervision and project administration, P.F., A.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hungarian grant NKFIH-1019658 and the Ministry of Innovation and Technology NRDI Office, Hungary within the framework of the Artificial Intelligence National Laboratory Program.  
Ethical review and approval were waived for this study due to its non-invasive and non-contact manner, and because it did not influence patient comfort or care. Written informed consent was obtained from parents to use the anonymised visual records for study purposes. Informed consent documents are stored as part of patient documentation. The study was lead by the head of the Division of Neonatology, Semmelweis University.  
Written informed consent was obtained from all subjects involved in the study.  
Restrictions apply to the availability of these data. Data was obtained from the participants given consent and are available from the authors only with the permission of the participants.  
The authors declare no conflict of interest.