Automatic neonatal sleep stage classification: A comparative study.

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.

A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.

Breast cancer is one of the leading causes of increasing deaths in women worldwide. The complex nature (microcalcification and masses) of breast cancer cells makes it quite difficult for radiologists to diagnose it properly. Subsequently, various computer-aided diagnosis (CAD) systems have previously been developed and are being used to aid radiologists in the diagnosis of cancer cells. However, due to intrinsic risks associated with the delayed and/or incorrect diagnosis, it is indispensable to improve the developed diagnostic systems. In this regard, machine learning has recently been playing a potential role in the early and precise detection of breast cancer. This paper presents a new machine learning-based framework that utilizes the Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and Multilayer Perception approaches to efficiently predict breast cancer from the patient data. For this purpose, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been utilized and classified using a hybrid Multilayer Perceptron Model (MLP) and 5-fold cross-validation framework as a working prototype. For the improved classification, a connection-based feature selection technique has been used that also eliminates the recursive features. The proposed framework has been validated on two separate datasets, i.e., the Wisconsin Prognostic dataset (WPBC) and Wisconsin Original Breast Cancer (WOBC) datasets. The results demonstrate improved accuracy of 99.12% due to efficient data preprocessing and feature selection applied to the input data.

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.

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.