The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In
this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy …

In this study, we examined the phase locking value (PLV) for seizure prediction, particularly,
in the gamma frequency band. We prepared simulation data and 65 clinical cases of …

Recent studies have demonstrated the disassociation between the mu and beta rhythms of
electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this …

Epilepsy is a neurological disorder affecting more than 50 million individuals in the world.
Analysis of the electroencephalogram (EEG) is a powerful tool to assist neurologists for …

In this study, a seizure prediction method is proposed based on a patient-specific approach
by extracting undulated global and local features of preictal/ictal and interictal periods of …

This study reports the design and implementation of a pattern recognition algorithm to
classify electroencephalographic (EEG) signals based on artificial neural networks (NN) …

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not
treated in a timely manner, it may cause serious complications. For timely treatment, an early …

Automatic seizure detection is of great importance for speeding up the inspection process
and relieving the workload of medical staff in the analysis of EEG recordings. In this study, a …

Automatic seizure detection has played an important role in the monitoring, diagnosis and
treatment of epilepsy. In this paper, a patient specific method is proposed for seizure …

This chapter presents a different design for reliable feature extraction for the classification of
epileptic seizures from multiclass EEG signals. In this chapter, we introduce a principal …