Classification of sympathetic skin response based on the morphologic features and Adaptive Neuro Fuzzy Inference system( ANFIS)

Abstract

Abstract The prevalence of polyneuropathy (PNP) or peripheral neuropathy (PN) is estimated to be 2%-3% in the general population and may be as high as 8% in people over 55 years of age. It’s the most common type of disorder of the peripheral nervous system in adults and in the elderly. Early detection and accurate classification of PNP can lead to proper diagnosis and treatment of painful symptoms. Our team developed a new method to classify the presence or absence of PNP in a database based on Adaptive Neuro Fuzzy Inference system( ANFIS) using sympathetic skin response (SSR) signal. To realize an efficient detection the output of our classification is divided into four classes such as the severity of PNP: no-PNP, mild, moderate, and severe class. In fact, we propose to extract the morphologic features of SSR signal including Latency, amplitude, rise time, the typical recovery time of 63%, and the typical recovery time of 50% which can be altered by PNP. Thus, the performances of the PNP severity classification system were compared with different machine learning (ML) algorithms such as support vector machine (SVM), K-nearest neighbor (KNN). Hence, The ANFIS model showed better performance in comparison to different ML models. In the classification stage, the best classification performance was achieved as 97.16%, 84.40%, and 87.12%% using ANFIS, KNN, and SVM classifier respectively.