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

Abstract

<b>Problem statement:</b> 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. <b>Approach:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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

Нейромережева Модель Прогнозування Параметрів Руху Судна В Системі Формування Надводної Обстановки Ближньої Морської Зони

“…Щодо цього відомий ряд робіт [8][9][10][11], авторами яких пропонуються різні варіанти моделювання руху об'єктів, обробки інформації, побудови алгоритмів виявлення несправностей, а також ідентифікації систем з використанням нейромережевих технологій. Роботи [12][13][14][15] присвячені нечіткому (або нейро-нечіткому) моделюванню руху об'єктів, побудові алгоритмів обробки даних, моделей підтримки прийняття рішення та ідентифікації систем.…”

Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro - Fuzzy Interface System

“…There is provided a system for the removal of the non-linear artifacts from respiratory signal, measured by MEMS based accelerometer sensor, through innovative ANFIS-based adaptive filter. The system combines an adaptive filter, integrating the Least Mean Square (LMS) and Recursive Least Square (RLS) algorithms, and ANFIS architecture [10]. In a similar study, to reduce the noise present in the pure respiratory signal is carried out a comparison of the an adaptive filtering based on ANFIS and filtering trough LMS and RLS [11].…”

Additive and Multiplicative Noise Removal Based on Adaptive Wavelet Transformation Using Cycle Spinning

“…In recent years, the image denoising problem has been widely studied and two main approaches have been developed: The variational methods (Aujol and Chambolle, 2005;Rudin et al, 1992;Chambolle, 2004;Osher et al, 2003;Gilboa et al, 2006) and wavelet techniques (Meyer, 2002;Donoho and Johnstone, 1994;1995a;Chang et al, 2000;Chambolle et al, 1998;Daubechies, 1990). Sankar et al (2012) proposed a new method to extract respiratory signal from the recorded abdomen movement using hybrid of neural and fuzzy inference system. Also, there is a rapidly increasing interest in designing hybrid methods using both wavelet shrinkage and the Total Variation (TV) denoising methods (Banazier and Yasser, 2011), but this method has shortcoming in the regions near to the edges in the image and its valid for speckle noise only.…”