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.
Nonlinear dynamics, artificial neural networks and neuro-fuzzy classifier for automatic assessing of tremor severity
Neurological diseases like Alzheimer, epilepsy, Parkinson's disease (PD), and other dementias influence the lives of patients, their families and society. In PD the brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptrons - MLP) and an Adaptive Neuro-Fuzzy Classifier (ANFC). In general, the results may be expressed as a prognostic (risk degree to contact PD).
Classification of parkinsonian and essential tremor using empirical mode decomposition and support vector machine
Taking into account two types of tremor, namely essential tremor (ET) and Parkinson@?s disease (PD), which are often misdiagnosed in clinical practice, a novel approach using singular value decomposition (SVD) to extract the features of intrinsic mode functions (IMFs) and support vector machine (SVM) is proposed to distinguish between them. The hand acceleration signals of 25 voluntary subjects with two conditions were collected and preprocessed. The empirical mode decomposition (EMD) method decomposed the signals into a number of stationary IMFs. The singular value features of the preceding four IMFs were extracted, and the features were inputted to the SVM classifier, which can recognize the two types of tremor. To comparing, we also used the singular value features of discrete wavelet transform (DWT) as input to the SVM. Cross-validation testing results indicated that the accuracy, sensitivity, and specificify of EMD-SVD features extracted are all remarkable higher than that of DWT-SVD method. Due to the accuracy, sensitivity, and specificify could arrive at 98%, 97.5% and 98.33% respectively, thus, practical guiding significance for diagnosing tremor types in clinic is provided.