A comparison between healthy and neurological disorders patients using nonlinear dynamic tools
In this paper, we evaluate the differences between Healthy subjects (HS), Parkinson Disease (PDS) subjects, and Epileptic subjects (EBS - Epileptic subject between seizures and ES - epileptic subjects in seizure) by computing some linear, statistic and non-linear features, such as: correlation dimension, maximum Lyapunov exponent and Hurst coefficient. A comparison is made between PDS, EBS, ES and HS groups using non-linear parameters. The EEG data are wavelet multiresolution decomposed into subbands of interest (alpha 8-12 Hz, beta 13-30 Hz, delta 0-4 Hz, theta 4-8 Hz, gamma 30-60 Hz). We applied rescaled range method to estimate the Hurst coefficient of the decomposed signals, with different types of wavelet transforms. The results obtained using the maximum Lyapunov exponent do not highlight very well differences between the 4 groups analyzed (HS, PDS, EBS and ES) but using Hurst coefficient we can make a very good differentiation of EEG signals. The results show that the Hurst coefficient is significant different for delta and theta rhythms extracted from the EEG signal in case of the healthy subjects compared with PDS or EBS subjects. The Hurst coefficient for healthy subjects has a value higher than 0.5 and for PD subjects or epileptic subject between seizures has a value lower than 0.5. Hence, the non-linear parameters such as Hurst coefficient can help in EEG interpretation and in neurological disorders diagnosis.
Detection of epileptic indicators on clinical subbands of EEG
Symptoms of epilepsy, which is characterized by abnormal brain electrical activity, can be observed on electroencephalography (EEG) signal. This paper employs models of chaotic measures on standard clinical subbands of EEG and aims to help detection of epilepsy seizures and diagnosis of epileptic indicators in interictal signals.
Spectral Subtraction and Cepstral Distance for Enhancing EEG Entropy
Electroencephalographic (EEG) signals are normally acquired in the presence of background noise which causes inaccurate or false entropy measurement throughout the signal. In this paper, spectral subtraction is used to pre-process EEG signals to improve the accuracy of computing the subband wavelet entropy (SWE). The silent period in the EEG signal is identified via cepstral distance which allows its entropy to be set to zero. The EEG signal presented in this paper represents various stages of brain recovery obtained from a rodent following global cerebral ischemia. The various subband entropies are calculated using wavelet decomposition in EEG subbands, namely delta, theta, alpha, beta and gamma. The utilization of spectral subtraction improved the accuracy of the SWE as compared to energy thresholding