| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 97 === The Epilepsy and epileptic seizure prediction algorithm by extracting useful features from Electroencephalography (EEG) is a hot topic in the current research of physiological signals. In view of the erroneous conclusions from the traditional statistical analysis methods for non-stationary and non-linear dynamics system of signals may affect the accuracy of forecasts.
This thesis presents a novel architecture based on wavelet and chaos theory, including Discrete Wavelet Transform (DWT), correlation dimension, and correlation coefficient. The wavelet transform is more suitable for non-stationary signals than Fast Fourier Transform (FFT) due to the ability of multi-resolution and time- frequency analysis. The fundamentals of Chaos theory for non-stationary and non-linear dynamics systems are more in line with the characteristics of brain waves than statistics. Therefore, combining DWT and Chaos analysis can achieve a high prediction rate.
In this thesis, first EEG signals are decomposed into several subbands. We predict seizures by the difference of convergent radius between the correlation dimension of EEG before a seizure and the one during a seizure for each subband. The proposed algorithm is evaluated with intracranial EEG recordings from a set of eleven patients with refractory temporal lobe epilepsy. In the experimental results, the algorithm with global settings for all patients predicted 87% of seizures with a false prediction rate of 0.24/h. Seizure warnings occur about 27 min ahead the ictal on average.
To apply the algorithm proposed to a portable physiological monitoring device, a seizure analysis circuit is also designed. Some techniques, such as lifting wavelet transform, enhanced memory addressing, and arithmetic reduction etc., are used to reduce circuit area and power consumption of circuit. In the future, the seizure analysis circuit can be further integrated into a digital signal processor for biomedical applications.
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