Hansen Disease Diagnostics using Wavelets, Fuzzy and EBP-NN based Expert System

摘要

Clinical electromyography (EMG) provides useful information for the diagnosis of Hansen Disease (HD) or leprosy. This paper deals with a computer based expert system which has been developed for the diagnosis of HD using the features of EMG signal recorded from the disease site and the symptoms obtained from the observation and interaction with the patient. In this study, the wavelet transform has been applied for feature extraction. The expert system uses the rms value and frequency domain parameters of the EMG signal and the symptoms under both the healthy and leprosy conditions of the subjects for the disease diagnostics using an artificial neural-network. The test results are promising and show that system can be successfully used for the identification of the leprosy condition right from its initiation stage and a large number of subjects can be saved from falling in the deadly trap of this disease.