Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30-second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen’s kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification.

For infants admitted at neonatal intensive care unit, the continuous monitoring of health parameters is critical for their optimal treatment and outcomes. So it’s crucial to provide proper treatment, accurate and comfortable monitoring conditions for newborn infants. In this paper, we propose wearable sensor systems integrated with flexible material based non-invasive sensors for neonatal monitoring. The system aims at providing reliable vital signs monitoring as well as comfortable clinical en vironments for neonatal care. The system consists of a smart vest and a cloud platform. In the smart vest, a novel stretching sensor based on Polydimethylsiloxane-Graphene (PDMS-Graphene) compound is created to detect newborns’ respiration signal; textile-based dry electrodes are developed to measure Electrocardiograph (ECG) signals; inertial measurement units (IMUs) are embedded to obtain movement information including accelerated speed and angular velocity of newborn wrists. Experiments were conducted to systematically test the sensing related characteristics of the aforementioned flexible materials and the performance of the proposed multi-sensor platform. The results show that the proposed system can achieve high quality signals. The wearable sensor platform is promising for continuous long term monitoring of neonates. The multimodal physiological and behavioral signals measured by the platform can be further processed for clinical decision support on the neonatal health status.

Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3.

In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFAR) approach in a hierarchical framework is proposed by coalescing the intensity-based method, pose estimation, and novel dedicated neonatal Face Feature Selection (FFS) algorithm. The intensity-based method is used for face detection, followed by the facial pose estimation algorithm and FFS are dedicated to neonatal pose and face feature recognition, respectively. In this study, video-data of 19 neonates’ were collected from the Children’s Hospital affiliated to Fudan University, Shanghai, to evaluate the proposed NFAR approach. The results show promising performance to detect the neonatal face, pose estimation (-450, 450), and facial features (nose, mouth, and eyes) recognition. The NFAR approach exhibits a sensitivity, accuracy, and specificity of 98.7%, 98.5%, and, 95.7% respectively, for the newborn babies at the frontal (00) facial region. The neonatal face and its attributes recognition can be expected to detect neonate’s medical abnormalities unobtrusively by examining the variation in newborn facial texture pattern.

Background: Necrotizing enterocolitis (NEC) is the leading cause of death among preterm infants born at < 30 weeks’ gestation. The incidence of NEC is reduced when infants are fed human milk. However, in many neonatal intensive care units (NICUs), it is standard practice to freeze and/or pasteurize human milk, which deactivates bioactive components that may offer additional protective benefits. Indeed, our pilot study showed that one feed of fresh mother’s own milk per day was safe, feasible, and can reduce morbidity in preterm infants. To further evaluate the benefits of fresh human milk in the NICU, a randomized controlled trial is needed.Methods: Our prospective multicenter, double-blinded, randomized, controlled trial will include infants born at < 30 weeks’ gestation and admitted to one of 29 tertiary NICUs in China. Infants in the intervention (fresh human milk) group (n = 1549) will receive at least two feeds of fresh human milk (i.e., within 4 h of expression) per day from the time of enrollment until 32 weeks’ corrected age or discharge to home. Infants in the control group (n = 1549) will receive previously frozen human milk following the current standard protocols. Following informed consent, enrolled infants will be randomly allocated to the control or fresh human milk groups. The primary outcome is the composite outcome mortality or NEC ≥ stage 2 at 32 weeks’ corrected age, and the secondary outcomes are mortality, NEC ≥ stage 2, NEC needing surgery, late-onset sepsis, retinopathy of prematurity (ROP), bronchopulmonary dysplasia (BPD), weight gain, change in weight, increase in length, increase in head circumference, time to full enteral feeds, and finally, the number and type of critical incident reports, including feeding errors.Discussion: Our double-blinded, randomized, controlled trial aims to examine whether fresh human milk can improve infant outcomes. The results of this study will impact both Chinese and international medical practice and feeding policy for preterm infants. In addition, data from our study will inform changes in health policy in NICUs across China, such that mothers are encouraged to enter the NICU and express fresh milk for their infants.Trial registration: Chinese Clinical Trial Registry; #ChiCTR1900020577; registered January 1, 2019;http://www.chictr.org.cn/showprojen.aspx?proj=34276