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 …

Most existing neonatal sleep staging appro-aches applied multiple EEG channels to obtain
good performance. However, it potentially increased the computational complexity and led …

Sleep plays an important role in neonatal brain and physical development, making its
detection and characterization important for assessing early-stage development. In this …

In this paper, we introduce a new variation of the Convolutional Neural Network Inception
block, called Sinc, for sleep stage classification in premature newborn babies using …

Objective To develop and validate an automated method for bedside monitoring of sleep
state fluctuations in neonatal intensive care units. Methods A deep learning-based algorithm …

In recent years, many diseases can be diagnosed in a short time with the use of deep
learning models in the field of medicine. Most of the studies in this area focus on adult or …

In quantifying the complexity characteristics of neurophysiological signals, the most
advanced entropy methods still have some inevitable limitations of poor accuracy …

Tedious parameter settings and poor performances seriously affect the entropy estimation's
effectiveness in time series analysis. To solve these limits, we propose a conceptually novel …

Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally,
experts use traditional methods based on every 30 seconds (s) of the biological signals …

Over the years, considerable research has been conducted to investigate the mechanisms
of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for …