Predicting Feeding Conditions of Premature Infants Through Non-Nutritive Sucking Skills Using a Sensitized Pacifier.

Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we present a new device that can measure both intraoral pressure (IP) and expression pressure (EP) as ororhithmic behavior parameters of non-nutritive sucking skills in infants. Our device is low-cost, easy-to-use, and accurate, which makes it appropriate for extensive studies. To showcase one of the applications of our device, we collected weekly data from 137 premature infants from 29 week-old to 36 week-old. Around half of the infants in our study needed intensive care even after they were 36 week-old. We call them full attainment of oral feeding (FAOF) infants. We then used the Non-nutritive sucking (NNS) features of EP and IP signals of infants recorded by our device to predict FAOF infants' sucking conditions. We found that our pipeline can predict FAOF infants several weeks before discharge from the hospital. Thus, this application of our device presents a robust and inexpensive alternative to monitor oral feeding ability in premature infants.

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video.

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.

An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex.

BACKGROUND AND OBJECTIVE: K-complexes, as a significant indicator in sleep staging and sleep protection, are an important micro-event in sleep analysis. Clinically, K-complexes are recognized through the expert visual inspection of electroencephalogram (EEG) during sleep. Since this process is laborious and has high inter-observer variability, developing automated K-complex detection methods can alleviate the burden on clinicians while providing reliable recognition results. However, existing methods face the following issues. First, most work only identifies the K-complexes in stage 2, which requires distinguishing the sleep stages as the prerequisite for further events' identification. Second, most approaches can only detect the occurrence of events without the ability to predict their location and duration, which are also essential to sleep analysis. METHODS: In this work, a novel hybrid expert scheme for K-complex detection is proposed by integrating signal morphology with expert knowledge into the decision-making process. To eliminate artifacts, and to minimize the individual variability in raw sleep EEG signals, the potential K-complex candidates are first screened by combining Teager energy operator (TEO) and personalized thresholds. Then, to distinguish signal shapes from background activity, a novel frame of filtering based on morphological filtering (MF) is devised to differentiate morphological components of K-complex waveforms from EEG series. Finally, K-complex waveforms are identified from the extracted morphological information by judgment rules, which are inspired by expert knowledge of micro-sleep events. RESULTS: Detection performance is evaluated by its application on the public database MASS-C1 (Montreal archives of sleep studies cohort one) which includes the recordings of 19 healthy adults. The detection performance demonstrates an F-measure of 0.63 with a recall of 0.81 and a precision of 0.53 on average. The duration error between events and detections is 0.10 s. CONCLUSIONS: The presented scheme has detected the occurrence of events. Meanwhile, it has recognized their locations and durations. The favorable results exhibit that the proposed scheme outperforms the state-of-the-art studies and has great potential to help release the burden of experts in sleep EEG analysis.

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

OBJECTIVE: In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL's), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. RESULTS: From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

Evaluation of Apgar Scores and Non-Nutritive Sucking Skills in Infants Using a Novel Sensitized Non-Nutritive Sucking System.

One of the challenges in examining development of newborns is measuring activities which are correlated to their health. Oral feeding is the most important factor in an infant's healthy development. Here, we present a new device that can measure intraoral and expression pressures produced in a newborn's mouth by non-nutritive sucking. We then develop a method to extract time-intervals that a sucking has occurred. To show an application of this device, we use Apgar score as a reference of the general health of newborns, and we evaluate these scores with the non-nutritive sucking patterns demonstrated by the infants. We show that for the pairs of infant with the same background but different Apgar scores, those with lower Apgar scores have lower pressure amplitudes while sucking. Importance of non-nutritive sucking skills in the development of newborns and ease of using our device make it useful for clinical studies of infantile health.

A knowledge-based approach for automatic quantification of epileptiform activity in children with electrical status epilepticus during sleep.

OBJECTIVE: Electrical status epilepticus during sleep (ESES), as electroencephalographic disturbances, is characterized by strong activation of epileptiform activity in the electroencephalogram during sleep. Quantitative descriptors of such epileptiform activity can support the diagnose and the prognosis of children with ESES. To quantify the epileptiform activity of ESES, a knowledge-based approach to mimic the clinical decision-making process is proposed. APPROACH: Firstly, a morphological operations-based scheme is designed to quickly locate the positive peaks/negative pits and roughly estimate the onset/offset of spike and slow-wave abnormalities. Then, to provide the accurate duration of ESES patterns, a set of rules for further adjusting these onsets/offsets are proposed by merging medical knowledge with a generalized threshold obtained from statistics. As such, the quantification is accomplished by evaluating the obtained spike and slow-wave abnormalities and their various durations. MAIN RESULTS: The effectiveness and feasibility of the proposed method were evaluated on a clinical dataset that collected at Children's Hospital of Fudan University, Shanghai, China. We demonstrate that the proposed method can recognize different types of spike and slow-wave abnormalities. The sensitivity, precision, and false positive rate achieved 91.96%, 97.09%, and 1.88 min-1, respectively. The estimation error for the spike-wave index was 2.32%. Comparison results showed that our method outperforms the state-of-the-art. SIGNIFICANCE: The quantification of spike and slow-waves provides information about ESES activity. The detection of variations types of spike and slow-waves improves the performance in the quantification of ESES. Experimental results suggest that the proposed method has great potential in automatic ESES quantification and can help improve the diagnosis and researches of epileptic encephalopathy with ESES.

Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection.

BACKGROUND: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).

Association between glycodelin and aryl hydrocarbon receptor in Iranian breast cancer patients: impact of environmental endocrine disrupting chemicals.

Breast cancer affects Iranian women one decade younger than their counterparts in other countries and the underlying risk factors have remained controversial. The aryl hydrocarbon receptor (AhR) mediates the effects of many environmental endocrine disruptors and contributes to the many other genes and Gd is an endocrine-regulated glycoprotein which may induce by AhR ligands in endometrium. This study has aimed to compare the interactions between Gd and AhR and other fundamental genes (p53, K-Ras, ER, PgR, AR) between pre and post menopausal Iranian breast cancer patients. To conduct immunohistochemical studies with appropriate monoclonal antibodies, 25 premenopausal invasive ductal carcinomas and 29 postmenopausal invasive ductal carcinomas were selected retrospectively in 2008-2010 from the pathology department of Imam Khomeini hospital complex of Tehran. Higher levels of AhR in epithelial cells of premenopausal patients and breast fibroadenoma emphasized the susceptibility of these cells to environmental induced tumors. Current study demonstrated a significant association between tumoral levels of Gd and AhR (p=0.002) in breast cancers which confirms the preliminary hypothesis about the role of TCDD exposure on Gd biosynthesis and secretion in TCDD-treated endometrial epithelial cells. In summary this study showed the dual prognostic role of Gd especially in premenopausal breast cancer which could be induced by AhR overexpression. Further studies are necessary to find the direct role of breast carcinogens as well as endocrine disrupting chemicals on the differential levels of Gd in breast tumors.