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.
Accurate measurements of output characteristics and detonations of motorbike high-speed racing engine and their optimization at actual atmospheric conditions and combusted mixture composition
Abstract At a motorbike high speed racing engine there is very important an output engine characteristics which depends significantly on a detonation combustion rate, atmospheric conditions and a combusted mixture composition. The combustion process has got a relevant influence on a wear degree of main engine parts or on their damage eventually. The detonation combustion rate is very important because this kind of burning can be not only destructive but a useful phenomenon, as well. If the process of detonation burning is kept on a certain level, it can help significantly to the next increasing of an engine power output. In this article there is described the measurements of output characteristics of a high-speed racing engine which were performed by means of a data-recording system and the detonation combustion was evaluated using a quantitative method. The actual values of atmospheric conditions and the mixture composition were added to the measured values. Then the obtained values are compared visually with a damage degree of an engine piston caused by the detonation burning. The result is a limit value expressed in a number of detonation units related to a run distance interval of the vehicle driven by the high-speed racing engine. In framework of this run distance interval there is the process of detonation burning profitable. As well as it increases an engine power output and together it is not destructive with regard to engine design parts.