Euclidean Distance based Adaptive Sampling Algorithm for Disassociating Transient and Oscillatory Components of Signals.

Neural signals encode information through oscillatory and transient components. The transient component captures rapid, non-rhythmic changes in response to internal or external events, while the oscillatory component reflects rhythmic patterns critical for processing sensation, action, and cognition. Current spectral and time-domain methods often struggle to distinguish the two components, particularly under sharp transitions, leading to interference and spectral leakage.

UMAP for Dimensionality Reduction in Sleep Stage Classification Using EEG Data.

Sleep is vital for wellness, and electroencephalography (EEG) serves as an instrumental tool in the study of sleep. Sleep is classified into four stages: stages N1-N3, and rapid eye movement (REM). To acquire effective and robust EEG features for sleep detection and analysis, we explore the dimensionality reduction effects of Uniform Manifold Approximation and Projection (UMAP) on various features of the EEG signals.

Positivity rate: an indicator for the spread of COVID-19.

Objective: This paper investigates the relationship between the positivity rate and numbers of deaths and intensive care unit (ICU) patients. In addition, it explores the use of the positivity rate as an indicator for the spread of COVID-19.

Methods: We used COVID-19 datasets for eight countries - Canada, USA, UK, Italy, Belgium, Ireland, Colombia and South Africa - and considered two correlation cases.

Dictionary learning for sparse representation and classification of neural spikes.

Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering.