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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Robust Design of Nonlinear Adaptive Hammerstein Filter Structure Using Evolutionary Algorithm: Real-Time Application

Shubham Yadav1, Suman Kumar Saha2, Rajib Kar3

  • 1Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India. shubham.ydv@gmail.com.

Cardiovascular Engineering and Technology
|January 6, 2026
PubMed
Summary
This summary is machine-generated.

This study enhances electrocardiogram (ECG) signal quality by removing artefacts using a novel adaptive Hammerstein filter optimized with the growth optimizer metaheuristic. The method significantly improves signal-to-noise ratio and reduces mean squared error for clearer heart signal analysis.

Keywords:
Adaptive noise cancellationElectrocardiogramGrowth optimiserHammerstein model

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Area of Science:

  • Biomedical Signal Processing
  • Computational Intelligence
  • Cardiovascular Engineering

Background:

  • Electrocardiogram (ECG) signals are low-amplitude, non-stationary biomedical data susceptible to external noise and artefacts.
  • Artefact removal is crucial for accurate ECG interpretation and diagnosis.
  • Existing methods often struggle with the complex, non-linear nature of ECG signals and superimposed noise.

Purpose of the Study:

  • To develop and evaluate an efficient artefact removal technique for ECG signals.
  • To optimize a nonlinear adaptive Hammerstein filter using metaheuristic algorithms.
  • To assess the performance of the growth optimizer in enhancing ECG signal quality.

Main Methods:

  • Designed a nonlinear adaptive Hammerstein filter structure.
  • Applied metaheuristic optimization algorithms, including the growth optimizer, particle swarm optimization, flower pollination algorithm, and marine predators' algorithm.
  • Tested the filter's efficacy on ECG signals corrupted with various artefacts like muscle noise and white Gaussian noise.

Main Results:

  • The growth optimizer-optimized adaptive Hammerstein filter demonstrated superior performance.
  • Achieved a significant improvement in signal-to-noise ratio (SNR) by 12 dB.
  • Attained a minimal mean squared error (MSE) of 3.698E-08.
  • Validated simulation results using a digital signal processor kit.

Conclusions:

  • The proposed growth optimizer-based adaptive Hammerstein filter effectively removes artefacts from ECG signals.
  • The technique offers a significant improvement over existing state-of-the-art methods.
  • This method is suitable for practical ECG signal enhancement applications.