Abstract

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Title:: Feature extraction and classification of electroencephalogram signal based on multifractal detrended fluctuation analysis

Author(s):: CHEN Jingkai1; MENG Xue1; WANG Changqing1; ZHONG Yading2; 1. School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China 2. Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei 230032, China

Keywords:: Keywords: electroencephalogram signal multifractal detrended fluctuation long short-term memory network feature extraction signal classification

Abstract:: Objective To propose a electroencephalogram (EEG) signal classification method based on the combination of feature extraction by multifractal detrended fluctuation analysis (MF-DFA) and long short-term memory network (LSTM) for solving the problems existing in EEG signal such as high data dimensionality and difficulty in prediction. Methods The multifractal spectrum of the EEG signal samples was firstly obtained by MF-DFA, and the functional relationship between the generalized Hurst exponent hq and the generalized dimensionality Dq was calculated. Then the multifractal spectrum was analyzed to find the most representative coordinate value as the signal eigenvector. Finally, the obtained signal eigenvector was used for LSTM training and classification test. The experiment was carried out on a processed epileptic EEG data set collected by University of Bonn. Results When the training samples accounted for more than 10% of the total samples, the test accuracy of LSTM classifiers stabilized at 98% and above and when the proportion was more than 80%, the test accuracy of LSTM classifier reached 100%. Even with a small number of training samples, the accuracy was higher than 95%. Conclusion The proposed algorithm has good accuracy and stability.