Polyneuropathy Early Detection Based on Electrodermal Activity Features and Support Vector Machines

In 1988, Fere discovered the Electrodermal activity (EDA) and it was defined originally as the property of human skins. Nowadays, it is well known as the characteristics of the human body that causes an incessant variation of the electrical skin potential. In this work, the EDA signal is used to detect the Polyneuropathy (PNP) disease. The main two steps of the proposed strategy is to extract several features via EDA signals and to classify them in two classes (Healthy case and PNP case) by using Support Vector Machine (SVM) algorithm. For this purpose, four different domains of feature extraction are investigated (morphology, time, frequency and time-frequency). The Emrirical Mode Decomposition (EMD) algorithm is used to decompose original EDA to some sub-signals ranged from high to low frequency order. Consequently, the time-frequency domain is investigated, and the EDA analyse is performed considering diffirent frequency ranges. Then, the extracted features were classified using SVM and 83.79% of accuracy was achieved. Compared to previous works, experimental results show that the proposed method is truthful for PNP detection.