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Range00:59

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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
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Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm.

Agya Ram Verma1, Yashvir Singh1, Bhumika Gupta1

  • 1Govind Ballabh Pant Engineering College, Pauri, Uttarakhand 246194 India.

Biomedical Engineering Letters
|January 4, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive artefact canceller using the bounded range artificial bee colony (BR-ABC) optimization technique for improved signal processing. The BR-ABC method significantly enhances signal-to-noise ratio and reduces errors in noisy environments.

Keywords:
Adaptive estimationAnt colony optimizationDesign optimizationGradient methodsMean square error methods

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Optimization Algorithms

Background:

  • Electromyography (EMG) signals are often corrupted by noise, impacting diagnostic accuracy.
  • Existing adaptive filtering techniques face challenges in effectively removing artifacts in highly noisy conditions.

Purpose of the Study:

  • To design and evaluate an adaptive artefact canceller using the bounded range artificial bee colony (BR-ABC) optimization technique.
  • To compare the performance of the BR-ABC based canceller against traditional methods like Recursive Least Square and Particle Swarm Optimization (PSO).

Main Methods:

  • An adaptive artefact canceller was developed utilizing the BR-ABC optimization algorithm.
  • Performance evaluation metrics included signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME), standard deviation (SD), and correlation factor (r).
  • The system was tested on EMG signals contaminated with power line and ECG noise at various SNR levels.

Main Results:

  • The BR-ABC algorithm demonstrated superior performance in noisy environments compared to other evolutionary algorithms.
  • The proposed ANC filter using BR-ABC achieved a 15 dB improvement in output average SNR.
  • Significant reductions in MSE (63%) and ME (83%) were observed compared to the PSO-based ANC filter.

Conclusions:

  • The BR-ABC technique provides a robust and effective solution for adaptive artefact cancellation in biomedical signal processing.
  • The developed ANC filter enhances the quality of EMG signals by improving SNR and reducing artifacts, leading to better correlation with the pure signal.