Recent years of research have shown that the complex temporal structure of ongoing
oscillations is scale-free and characterized by long-range temporal correlations. Detrended …

Noise reduction is important for signal analysis. In this paper, we propose a hybrid denoising
method based on thresholding and data-driven signal decomposition. The principle of this …

Scaling properties are one of the most important quantifiers of complexity in many events, as
time series (TS). To try to have a glimpse how brain is working, we need new methods of …

A quantitative and objective assessment of background electroencephalograph (EEG) in
sick neonates remains an everyday clinical challenge. We studied whether long range …

Brain activity is composed of oscillatory and broadband arrhythmic components; however,
there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal …

Photoplethysmography (PPG) signals are widely used in clinical practice as a diagnostic
tool since PPG is noninvasive and inexpensive. In this article, machine learning techniques …

Background Cortical high-frequency activation immediately before death has been reported,
raising questions about an enhanced conscious state at this critical time. Here, we analyzed …

We study short-term and long-term persistence properties (related with auto-correlations) of
amplitudes and frequencies of EEG oscillations in 176 healthy subjects and 40 patients …

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during
voluntary movement intention. Characterization of such changes has focused mostly on …

Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent
chronic disease with a significant global impact. The timely detection of diabetes in patients …