Implementation of Robust optimized Adaptive Algorithms for ECG Noise Reduction
Objective
The objective of this study is to optimize adaptive noise cancellation in ECG signals using swarm intelligence algorithms—SOS, PSO, and HS—to enhance noise reduction performance while minimizing computational complexity in real-time biomedical applications.
Abstract
Noise cancellation and abnormality detection in ECG signal is a tedious task in real time environment. These noises arise due to power line interference and human movements. There are various digital filters readily available to reduce noise in ECG signal but the computational cost and complexity is high. Hence, noise removal from ECG signal is an important matter of concern. In this paper, swarm intelligence techniques for the optimization purpose of adaptive filters/noise canceler (ANC) are utilized in the biomedical signal processing field. The analysis of results for de-noising ECG signals through adaptive filtration is presented using the symbiotic organisms search (SOS), particle swarm optimization (PSO) and harmony search (HS) algorithms which are applied to estimate and adjust the parameters of the adaptive filter. The proposed optimization techniques will evaluate the ECG noise reduction performance in terms of signal to noise ratio (SNR), mean square error (MSE), maximum error (ME), mean difference (MD), and normalized root mean squared error (NRMSE).
Keywords: Active Noise Control, Adaptive filters, ECG, PSO, MSE, NRMSE.
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 R2022b.
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.
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
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
