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.
A Review on Image Classification Techniques to classify Neurological Disorders of brain MRI
Neurological disorders have more than 600 brain disease. Therefore it is very complicated task to detect and classify the brain MRI data. To classify the brain MR image by many classification techniques such as K-nearest neighbor (KNN), decision tree (DT), Support vector machine (SVM), neural network and convolutional neural network, we have study and compered K-nearest neighbor, support vector machine, decision tree, neural network and convolution neural network . In this review paper we explain which classification technique is better for detection of brain MRI data set. The detection for normal and abnormal brain MRI, the CNN improve the accuracy as compare to other classification.
Nonlinear dynamic analysis of oscillatory repetitive movements in Parkinson's disease and essential tremor
Uncertainty exists on whether Parkinson's disease (PD) and essential tremor (ET) patients have similar degree of impairment during motor tasks. We investigated this problem by analyzing nonlinear dynamics of repetitive movements in 21 control subjects, 33 mild‐moderate PD patients, and 18 ET patients. Accelerometer signals were recorded during finger tapping and unbounded forearm movements between two points, and processed with moving average filtering to generate a new signal consisting of the temporal distance between consecutive cycles. We calculated: mean interpeak interval (slowness), interpeak interval variability (irregularity), and beat decay (BD) of the auto mutual information (AMI) value, which estimates signal predictability by measuring the loss of signal information over a timescale. Both PD and ET had longer interpeak interval (except for finger tapping), higher interpeak interval variability, and higher BD‐AMI values than controls (P ≤ 0.007, all comparisons). ET patients had higher BD‐AMI values than PD (P = 0.003). BD‐AMI was the parameter that discriminated better between subjects (diagnosis accuracies about 80%). No differences existed between PD patients with and without tremor or between PD or ET patients with different disease stages, for any parameter. Evaluation of nonlinear dynamics of oscillatory repetitive movements is a feasible and promising tool for studying movement physiology. Movement performance is more predictable in PD and ET than in controls, even in early disease stages. Slowness and irregularity of movement in PD and ET cannot be fully explained by tremor. Some common pathogenic mechanisms leading to bradykinesia may contribute to this impairment. © 2010 Movement Disorder Society