Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks. 3525 30-s EEG segments from 19 infants are used to train and test the proposed models. There are twelve time and frequency domain features extracted from each channel. Each model receives the common features of nine channels as an input vector of size 108. Each model’s performance was evaluated based on a variety of evaluation metrics. The maximum mean accuracy of 84.78% and kappa of 69.63% has been obtained by the AutoML-based Random Forest estimator. This is the highest accuracy for EEG-based sleep-wake classification, until now. While, for the AutoML-based Adaboost Random Forest model, accuracy and kappa were 84.59% and 69.24%, respectively. High performance achieved in the proposed autoML-based approach can facilitate early identification and treatment of sleep-related issues in neonates

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Peer reviewe

Predicting the Standard and Deviant Patterns In EEG Signals Based On Deep Learning Model

In the recent years, there has been a significant growth in the area of brain computer interference. The main aim of such area is to read the brain activities, formulate a specific/desired output and power a specific approach using such output. Electroencephalography (EEG) may provide an insight into the analysis procedure of the human behavior and the level of the attention. Using the deep learning based neural network has a great success in different applications recently,such as making a decision, classifying a pattern and predicting an outcome by learning from a set of data and build the right weight matrices to represent the prediction outcome or the learning patterns. This research work proposes a novel model based on long short-term memory network to predict the standard and the deviant cases within EEG data sets. The EEG signals are acquired utilizing all the 128 electrodes that represent the 128 channels from infants aged between 5 and 7 months. Statistical approaches, principal component analysis (PCA) and autoregressive (AR) power spectral density estimate have been employed to extract the features from the EEG data sets. The proposed deep learning based model has shown great robustness dealing with different types of features extracted from the processed data sets. Very promising results have been achieved in predicting the standard and deviant cases. The standard case was presented with frequent, repetitive stimulus and the deviant case was presented with infrequent sounds

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler\u27s connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants\u27 age within ± 3.6 months error and a prediction