From 1990 to 2024, this study presents a groundbreaking bibliometric and sentiment
analysis of nanocomposite literature, distinguishing itself from existing reviews through its …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

This work presents SeizFt—a novel seizure detection framework that utilizes machine
learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by …

Speech-based approaches for assessing Parkinson's Disease (PD) often rely on feature
extraction for automatic classification or detection. While many studies prioritize accuracy by …

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep
pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert, which …

The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent
and requires experienced specialists to annotate seizure events. It is clinically time …

The present study focuses on addressing the issue of too frequent triggers of deep
convection in climate models, which are primarily based on physics-based classical trigger …

Study objectives To develop a non-invasive and practical wearable method for long-term
tracking of infants' sleep. Methods An infant wearable, NAPping PAnts (NAPPA), was …

We developed an inherently interpretable multilevel Bayesian framework for representing
variation in regression coefficients that mimics the piecewise linearity of ReLU-activated …

Sleep disorder classification is crucial in improving human quality of life. Sleep disorders
and apnoea can have a significant influence on human health. Sleep-stage classification by …