Epileptic Seizure Prediction Based on Zero-Crossing Interval Features of Scalp EEG Signals

• Main Authors: Bo-Jhong Chen, 陳柏仲
• Other Authors: Chen-Sen Ouyang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/c8693w

碩士 === 義守大學 === 資訊工程學系 === 105 === In this study, we propose an approach of epileptic seizure prediction by combining the zero-crossing intervals of scalp EEG signals and heart rate variability analysis. In this study, we propose an online fuzzy extreme learning machine based on the recursive singular value decomposition for improving the fuzzy extreme learning machine, and therefore making it applicable for solving online learning problems in classification or regression modeling. Like the original fuzzy extreme learning machine, our approach randomly assigns values to weights of fuzzy membership functions in the hidden layer. However, the Moore-Penrose pseudoinverse is replaced with the recursive singular value decomposition for calculating the optimal weights corresponding to the output layer. Compared with the original fuzzy extreme learning machine, our approach is applicable for the online learning of classification or regression modeling and produces the same modeling accuracy. Moreover, our approach possesses the better modeling accuracy and stability than the other approach, namely, online sequential learning algorithm.