The analysis of EEG signals plays a vital role in the detection of Seizure. The EEG signal of a normal person varies when compared to that of a seizure affected person. A new wavelet is created which closely represents a normal EEG wave. The discrete wavelet transform using the new wavelet family is applied to the input EEG signals. Since the new wavelet represents the normal EEG wave pattern t...

The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities. The detection of epileptic seizures based on EEG signal is very useful in diagnostics. In this paper, we present a new method for discrimination between seizure and seizure-free EEG signals. The proposed method is based on empirical mode decomposition (EMD) process. We investigated that th...

In this paper, we present a new method based on empirical mode decomposition (EMD) for classification of seizure and seizure-free EEG signals. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The method proposes the use of the area parameter and mean frequency estimation of IMFs in t...

A stroke is a disruption of blood supply to the brain that leads disability. Rehabilitation can reduce damage and other complications also recover disability due stroke. One instrument be used in post-stroke patients monitoring Electroencephalogram (EEG). EEG signals are recorded from several channel pairs. So apart maintaining signal sequences, handling time connectivity between channels essen...

The embedding dimensions of normal and epileptic electroencephalogram (EEG) time series are analyzed by two different methods, Cao's method and differential entropy method. The results of the two methods indicate consistently that the embedding dimensions of EEG signals during seizure will change and become different from that of normal EEG signals, and the embedding dimensions will vary intens...