The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which
has affected million people in the world. Hence, an early detection of the focal epileptic …

Over the past decade, converging evidence from diverse studies has demonstrated that
sleep is closely associated with the mental and physical health, quality of life, and safety …

There is an increasing interest in applying artificial intelligence techniques to forecast
epileptic seizures. In particular, machine learning algorithms could extract nonlinear …

Electroencephalography (EEG) is a measurement tool to measure the electrical activity of
brain observed due to chemical variation in brain. The EEG analysis has important role in …

In this paper, we present an approach for the anticipation of electroencephalography (EEG)
seizures using different families of wavelet transform. Different signal attributes are …

The prediction of epileptic seizures is crucial to aid patients in gaining early warning and
taking effective intervention. Several features have been explored to predict the onset via …

Objective. The cyclic alternating pattern is a marker of sleep instability identified in the
electroencephalogram signals whose sequence of transient variations compose the A …

The recent scientific literature abounds in proposals of seizure forecasting methods that
exploit machine learning to automatically analyze electroencephalogram (EEG) signals …

The aim of this study is to implement an automatic system to detect the activation phases of
Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon composed of consecutive …

Common similarity measures of time domain signals such as cross-correlation and Symbolic
Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is …