Research On Epileptic EEG Identification And Automatic Seizure Detection

Epilepsy is a chronic neurological disorder characterized by an ongoing liability to recurrent epileptic seizures that result from abnormal, excessive or hypersynchronous neuronal activity in the brain. As an important tool for research of epileptic seizures, electroencephalogram (EEG) contains a mass of physiological and pathological information which cannot be offered by other physiology methods. The automatic seizure detection based on signal processing and pattern recognition is significant in both relieving heavy workload of doctors and improving the diagnosis efficiency for epilepsy.At present, the application of the nonlinear dynamics in the analysis of EEG signals brings more rich information to the identification of ictal EEGs. But the high computational cost of most of nonlinear EEG features blocked the real-time seizure detection. Meanwhile the conventional methods of "EEG feature extraction+classifier" usually compute several kinds of EEG features, and then organize them as feature vectors or carry out feature selection. Such ways not only increase computation complexity further, but also add the problem of EEG feature selection. This thesis is based on the research of EEG feature extraction, epileptic EEG recognition and automatic seizure detection, and conducts the following studies in the aspects of EEG nonlinear feature extraction, EEG fractal characteristics, and EEG classification via the theory of sparse representation.Firstly, as an important branch of the nonlinear dynamics, the fractal geometry theory is introduced to the analysis and process of EEG signals. The differential box-counting approach for estimating the fractal dimension of an image is improved to fit for one-dimensional EEG signals. Compared to the box-counting dimension, the fractal intercept has stronger discriminatory power for the classification of ictal and interictal EEGs. Afterwards, the blanket technique is introduced to calculate the multi-scale blanket dimension and fractal intercept of EEG signals. It is found that there are obvious changes of their values at different scales before the occurrence of the epileptic seizure.Secondly, the novel seizure detection and prediction methods are proposed based on these EEG fractal features. The EEG fractal intercept via differential box-counting approach is combined with extreme learning machine (ELM) to build a detection method which is suited for multi-channel continuous EEG signals. And then the BLDA classifier is trained to discriminate preictal from interictal patterns of the multi-scale blanket dimension and fractal intercept, which enable to predict seizure events. The experimental results not only proved the validity of the presented EEG fractal features, but also showed the good performance of the proposed seizure detection and prediction methods.Thirdly, a novel EEG classification method based on kernel sparse representation is presented. In the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l1-minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized through the comparison of the residuals. Unlike the conventional EEG classification methods, the calculation and choice of EEG features are avoided in this framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The kernel SRC method shows superior performance in the experiments of EEG classification.Finally, following the SRC scheme, the l1-minimization problem is replaced with l2-minimization to estimate the sparse coefficients of test EEG sample analytically so that the time-consuming iterative operations are avoided. This improved method emphasizes the key role played by using all training samples to collaboratively represent the test EEG sample, so it is named collaborative representation based classification (CRC) method. Besides, the kernel trick is combined with the CRC method. The decision variable is defined as the residual associated with the non-seizure training samples minus the residual associated with the seizure training samples. And then the post-processing including the smoothing technique is added. Thus a novel seizure detection method for long-term EEG recordings is proposed based on the kernel collaborative representation method. The experiments on the long-term EEG dataset indicate that this detection method not only achieves satisfactory results but also has the potential for the real-time seizure detection.The research work in this thesis contributes to the development of the study of automatic seizure detection in the aspects of technique theories, algorithms and clinical application. This thesis also promotes the research on nonlinear EEG feature extraction, the application of fractal theory in analysis of EEG signals, and EEG classification via sparse representation. Due to the limitation of the used EEG database size, the proposed ictal EEG identification and seizure detection methods are need to be evaluated further on a much larger range of EEG data.