Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Author:

Tallapragada V. V. Satyanarayana1ORCID,D. Venkat Reddy2,K. N. V. Suresh Varma3,D. V. N. Bharathi3

Affiliation:

1. Sree Vidyanikethan Engineering College, India

2. Mahatma Gandhi Institute of Technology, India

3. S. R. K. R. Engineering College, India

Abstract

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

Publisher

IGI Global

Subject

General Computer Science

Reference24 articles.

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4. Particle filtering approach to membership function adjustment in fuzzy logic systems

5. The effects of noise on computerized electrocardiogram measurements

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