Resting state EEG-based sudden pain recognition method and experimental study. (May 2020) - Cao, Tianao; Wang, Qisong; Liu, Dan; Sun, Jinwei; Bai, Ou
Highlights: The sudden pain, which has not been so common with other pain such as the cold pain and chronic pain, is researched and a complete set of pain experiment platform is built. Not only the traditional classic methods such as Power Spectral Density (PSD) and Support Vector Machine (SVM), but also the novel SBELM is utilized to get the higher accuracy by optimizing the features. Every channel has been compared and the preferable channels are selected, which could provide guidance to doctors to treat the illness of patients effectively. Abstract: Pain is a sensory phenomenon when the body hurts and receptors are stimulated. Although pain activates the body's protective mechanism, some excessive pain reactions will damage the nearby biological tissues, and mostly it will bring people severe mental distress. In particular, some individuals with cognitive impairment, like the infants, are unable to describe their own pain, and thereby the disease will be delayed. These types of pain require intervention and relief and sudden pain belongs to one of them. Therefore, this paper proposes on a method of sudden pain recognition based on resting state EEG signals. This method can recognize the presence of sudden pain, distinguish the location of pain and can be effectively applied to disease diagnosis. The platform of sudden pain stimulation and EEG acquisition system was designed and built, and a series of experiments were carried out for different subjects. We preprocessed the raw EEG signals and extracted features via Power Spectral Density (PSD) and Multifractal Detrended Fluctuation Analysis (MF-DFA). We also utilized the Support Vector Machine (SVM), Sparse Bayesian Extreme Learning Machine (SBELM) and D-S Evidence Theory to do classification, utilizing 10-Fold Cross-validation. The results suggested that the accuracy of judging the presence of pain was up to 89.3 % on average, accuracy of pain location discrimination was up to 81.3 % on average, and accuracy of cross validation was up to 90.1 %.