Improving SNR, MSE and Denoising of EMG-Contaminated ECG Signals using EMD
Objective
This study aims to enhance ECG clarity by using Empirical Mode Decomposition (EMD) to effectively suppress EMG noise without significant signal distortion.
Abstract
An electrocardiogram's (ECG) clinical interpretation may be negatively impacted by noise. The Electromyographic (EMG) noise has a spectrum overlap with the QRS complex, making its removal very difficult. Diagnostically significant information is obscured by the frequent distortion of signal morphology caused by the current EMG-denoising methods. Techniques: This article proposes a novel Empirical Mode Decomposition (EMD) for effective suppression of EMG noise. The primary theory is that noise can be extracted with the least amount of signal change possible by temporarily removing the prominent ECG components. The MIT-BIH arrhythmia database, the SimEMG database of simultaneously recorded reference and noisy signals, and the synthetic ECG signals, both with noise from the MIT Noise Stress Test Database, are used to validate the approach.
Keywords: Mobile ECG, EMG Noise, ECG acquisition, filtering, EMD Decomposition.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Signal Processing?
· About Signal Processing
· Introduction to Signal Processing
· How analog and digital signal is formed
· Importing the signal via signal acquisition tools
· Analyzing and manipulation of signals.
· Phases of signal processing:
· Acquisition
· Signal enhancement
· Signal restoration
· Medical Signal Processing
· Medical Signal Analysis
· Medical Signal Diagnosis
· Filtering techniques
· Machine Learning Algorithms
· Deep Learning Algorithms etc.
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
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