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.
Spatial analysis of EEG signals for Parkinson’s disease stage detection
Diagnosis of Parkinson’s disease (PD) in the early stages is very critical for effective treatments. In this paper, we propose a simple and low-cost biomarker to diagnose PD, using the electroencephalography (EEG) signals. In the proposed method, EEG is used to detect the brain electrical activities in internal regions of brain, e.g., basal ganglia (BG). Based on the high correlation between PD and brain activities in the BG, the proposed method provides a highly accurate PD diagnostic measure. Moreover, we obtain a quantitative measure of the disease severity, using the spectral analysis of extracted brain sources. The proposed method is denoted by Parkinson’s disease stage detection (PDSD). The PDSD includes brain sources separation and localization steps. The accuracy of the method in detection and quantification of PD is evaluated and verified by using information of ten patients and ten healthy people. The results show that there is a significant difference in the number of brain sources within the BG region, as well as their power spectral density, between healthy cases and patients. The accuracy and the cross-validation error of PDSD to detect PD are 95% and 6.25%, respectively. Furthermore, it is shown that the total power of extracted brain sources within the BG region in the $$\alpha $$ α and $$\beta $$ β rhythms can be used effectively to determine the severity of PD.
Analysis of multichannel EEG synchronization before and during generalized epileptic seizures
The genesis of seizures in idiopatic generalized epilepsy (IGE) is investigated in a population of 10 patients (5 affected by Juvenile Absence Epilepsy, or JAE, and 5 affected by Juvenile Myoclonic Epilepsy, or JME). The aim of the study is the identification of a talamic or cortical origin of the paroxistic activity. The mutual synchronization among EEG channels was evaluated trough the cross conditional corrected entropy (CCCE) calculated on the row signals and after the application of the Laplacian operator. In JAE patients a leading role of the left frontal lobe was put into evidence in correspondence of the seizure, while in JME patients a leading electrode was not identified, suggesting a possible talamic origin of the seizure.