A New Self-Adaptive Neuro Fuzzy Inference System for the Removal of Non-Linear Artifacts from the Respiratory Signal

Abstract

Problem statement: In this study, a new ANFIS-based adaptive filter is proposed to remove the non-linear artifacts from the respiratory signal measured using MEMS based accelerometer sensor. The data recorded from the abdomen movement includes the respiratory signal, electromyogram, 50Hz power line interference and the random electrode noise. In order to avoid convergence into local extremes, the system employs ANFIS method. Approach: The proposed architecture is a combination of adaptive filter in which Least Mean Square and Recursive Least Square algorithms are employed and ANFIS, where ANFIS is recruited whenever the adaptive filter is suspected of reading a local extreme value. Results: The results showed that the normalized LMS performs better when compared to other LMS algorithms with SNR improvement of 4.17 dB and MSE value of 0.062. RLS provides least MSE value or 0.015 but only with highest filter order. Quantitative analysis reveals that ANFIS out performs the normalized LMS and RLS algorithms. Conclusion: The result obtained indicates that ANFIS is a useful Artificial Intelligence technique to cancel the non linear interferences from the respiratory signal with very low mean square value of 0.011.