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Ghosh Dastidar, SamanwoyModels of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks
Doctor of Philosophy, The Ohio State University, 2007, Biomedical Engineering
A multi-paradigm approach integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy diagnosis and seizure detection. This research challenges the assumption that the EEG represents the dynamics of the entire brain as a unified system. It is postulated that the sub-bands yield more accurate information about constituent neuronal activities underlying the EEG. Consequently, certain changes in EEGs not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. A novel wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma sub-bands of EEGs for detection of seizure and epilepsy. The methodology is applied to three different groups of EEGs: healthy subjects, epileptic subjects during a seizure-free interval (interictal), and epileptic subjects during a seizure (ictal). Two potential markers of abnormality quantifying the non-linear chaotic EEG dynamics are discovered: the correlation dimension and largest Lyapunov exponent. A novel wavelet-chaos-neural network methodology is developed for EEG classification. Along with the aforementioned two parameters, the standard deviation (quantifying the signal variance) is employed for EEG representation. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy (96.7%). To increase the robustness of classification, a novel principal component analysis-enhanced cosine radial basis function neural network classifier is developed. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network employed in the second stage significantly. The new classifier is as accurate as LMBPNN and is twice as robust. Next, biologically realistic artificial neural networks are developed to reach the next milestone in artificial intelligence. First, an efficient spiking neural network (SNN) model is presented using three training algorithms: SpikeProp, QuickProp, and RProp. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Next, a new Multi-Spiking Neural Network (MuSpiNN) and supervised learning algorithm (Multi-SpikeProp) are developed. Finally, the models are applied to the epilepsy and seizure detection problems to achieve high classification accuracies.


Hojjat Adeli (Advisor)


Temporal Lobe Epilepsy; Electroencephalogram (EEG); EEG Classification; Epilepsy Diagnosis; Seizure Detection; Wavelet Transform; Chaos Theory; Artificial Neural Networks; Spiking Neural Networks; Principal Component Analysis; Cosine Radial Basis Function

Master of Science (M.S.), University of Dayton, 2010, Electrical Engineering
There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and have generally utilized more accurate neuron models, such as the Izhikevich and Hodgkin-Huxley models, in favor of the more popular integrate and fire model. This thesis examines the feasibility of using GPGPUs for accelerating a spiking neural network based character recognition network to enable large scale neural systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA GPGPU platforms and one GPGPU cluster were examined. These include the GeForce 9800 GX2, the Tesla C1060, the Tesla S1070 platforms, and the 32-node Tesla S1070 GPGPU cluster. Our results show that the GPGPUs can provide significant speedups over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided speedups of 5.6 and 84.4 time over highly optimized implementations on the fastest CPU tested, a quad core 2.67 GHz Xeon processor, for the Izhikevich and Hodgkin Huxley models respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPGPUs are well suited for this application domain. A large portion of the results presented in this thesis have been published in the April 2010 issue of Applied Optics [1].


Tarek Taha (Committee Chair); John Loomis (Committee Member); Balster Eric (Committee Member)


Electrical Engineering


Spiking neural networks; GPGPU