A bewildering variety of methods for analysing sleep EEG signals can be found in the
literature. This article provides an overview of these methods and offers guidelines for …

The analysis of electroencephalography (EEG) recordings has attracted increasing interest
in recent decades and provides the pivotal scientific tool for researchers to quantitatively …

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …

The present work aims at automatic identification of various sleep stages like, sleep stages
1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness from single …

The automatic detection and classification of epileptic EEG are significant in the evaluation
of patients with epilepsy. This paper presents a new EEG classification approach based on …

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very
tedious to interpret visually and highly difficult to extract the significant features from them …

Time series measured in real world is often nonlinear, even chaotic. To effectively extract
desired information from measured time series, it is important to preprocess data to reduce …

The only integrative approach to chaos and random fractal theory Chaos and random fractal
theory are two of the most important theories developed for data analysis. Until now, there …

Higher brain functions depend upon the rapid creation and dissolution of ever changing
synchronous cell assemblies. We examine the hypothesis that the dynamics of this process …

Early prediction of person at risk of Sudden Cardiac Death (SCD) with or without the onset of
Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) still remains a continuing …