Epilepsy Electroencephalogram Signal Classification Method Based on Multi-Band Path Signature Features

Automatic detection of epilepsy based on electroencephalogram (EEG) signals is greatly helpful for clinical diagnosis and treatment of epilepsy. Most epilepsy detection algorithms ignore the temporal relation of EEG signals, therefore, this paper proposed an epilepsy EEG signal classification method based on multi-band path signature features. Firstly, EEG signals were decomposed into five frequency bands. Secondly, features were extracted using the path signature (PS) algorithm. Thirdly, features were fused after local principal component analysis (LPCA) removed the feature’s correlation. Finally, an ensemble classifier was used to predict epilepsy. Since the path signature can dig into the correlation of EEG signals, combined with local principal component analysis, the method proposed in the paper can obtain more discriminative epilepsy classification features. The comparative experiments of 10-fold cross-validation were conducted to validate this method on two datasets, i. e., the private dataset from a local hospital with more than 2000 seconds of segments and the CHB-MIT epilepsy dataset. The results show that the average classification accuracy of the method reached 97.25% on the private dataset, which is higher than those of the classical EMD (Empirical Mode Decomposition) method and the up-todate LSTM (Long Short-term Memory Network) + CNN (Convolutional Neural Network) method by 3.44 and 1.35 percentage points respectively. Moreover, the proposed method can achieve an average classification accuracy of 98.11% on the CHB-MIT dataset, which is higher than those of the classical EMD method and the up-to-date LSTM+CNN method by 5.20 and 2.64 percentage points respectively, and this method achieves the best classification accuracy than other methods on both datasets.