Abstract

Info

Author(s):: LI Dong1; JIN Tao1; FENG Zhiying2; ZUO Lulu1; ZHU Xiang1; LIUWeiming1; 1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; 2. Department of Pain, the First Affiliated Hospital, Zhejiang University, Hangzhou 310027, China

Keywords:: electroencephalogram; pain intensity recognition signal; postherpetic neuralgia; feature extraction; random forest

Abstract:: Abstract: Objective To perform feature extraction and feature selection for electroencephalogram (EEG) signals collected from patients with postherpetic neuralgia for quantitatively evaluating the level of pain. Methods Discrete wavelet transform was employed to decompose clinically collected EEG signals to obtain approximate and detail coefficients. The feature vectors were composed of sub-band energy ratio, coefficient statistics, sample entropy and phase-locked value which were calculated based on the decomposition coefficients of each level. Random forest was used for feature selection and pain intensity recognition. Results The proposed method realized the 3 classifications of pain levels, with an average classification accuracy of 91.7%. Moreover, the accuracy of the classification between no-pain and high-pain reached 100%. Conclusion The proposed method can be used to effectively extract and select features from EEG signals, and realize pain intensity recognition with a high accuracy, which lays a foundation for the objective evaluation of clinical pain.