Supervised Learning-Based Noise Detection to Improve the Performance of Filter-Based ECG Signal Denoising

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 6

Year of Publication : 2023

Authors : Veerabomma Supraja, Pasumarthy Nageswara Rao, Mahendra Nanjappa Giri Prasad

10.14445/23488549/IJECE-V10I6P105

How to Cite?

Veerabomma Supraja, Pasumarthy Nageswara Rao, Mahendra Nanjappa Giri Prasad, "Supervised Learning-Based Noise Detection to Improve the Performance of Filter-Based ECG Signal Denoising," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 6, pp. 35-51, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I6P105

Abstract:

A significant purpose in detecting pervasive computing approaches is to improve the specificity and sensitivity of arrhythmia detection using electrocardiograms. Because ECG signals frequently propagate over distributed computer settings such as the medical Internet of things, noise is prevalent (medical IoT). In these distributed and widespread computing-aided methodologies, noisy electrocardiograms are common to false alarms. The fundamental goal of the machine learning-based arrhythmia detection algorithms discussed in this publication is to detect noise scope in electrocardiograms. The suggested approach determines whether or not the provided electrocardiograms are influenced by noise. In this regard, the approach takes advantage of the electrocardiogram's temporal and spectral characteristics. The performance of the suggested technique was evaluated using multifold cross-validation. In addition, a comparative study was performed comparing the average of peak Signal to Noise Ratio, respective standard deviation and filter sequence length obtained from filtering noise by FIR and IIR filters from raw ECG signals and the SLND-selected noisy ECG signals.

Keywords:

Electro Cardiogram (ECG), Baseline Wandering (BA), Powerline Interference (PLI), Weiner Filter (WF), Fourier-transform (FT), FIR, IIR.

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