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An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD).

Ahmed F Hussein1, Warda R Mohammed1, Mustafa Musa Jaber2,3

  • 1Biomedical Engineering Department, College of Engineering, Al-Nahrain University, Baghdad 10072, Iraq.

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This study introduces an adaptive Empirical Mode Decomposition (EMD) method to remove noise from electrocardiogram (ECG) signals. The technique effectively reduces noise with minimal distortion, improving diagnostic accuracy in automated systems.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing cardiac disorders.
  • ECG signals are susceptible to noise during acquisition and processing, hindering accurate interpretation.
  • Existing noise reduction methods often compromise signal integrity.

Purpose of the Study:

  • To propose an adaptive Empirical Mode Decomposition (EMD) based technique for effective ECG noise removal.
  • To minimize signal distortion while significantly reducing noise in ECG recordings.
  • To enhance the diagnostic capabilities of automated medical systems through cleaner ECG data.

Main Methods:

  • An adaptive noise removal technique based on Empirical Mode Decomposition (EMD) is developed.
  • The method processes ECG signals to eliminate noise, focusing on preserving signal characteristics.
  • Performance is evaluated using standard metrics like Mean Square Error and Signal-to-Noise Ratio (SNR) changes.

Main Results:

  • The proposed EMD-based method demonstrates significant noise reduction in ECG signals.
  • The technique achieves low signal distortion, outperforming traditional EMD approaches.
  • Evaluations on MIT-BIH and BUT QDB databases confirm the method's effectiveness across various SNR levels.

Conclusions:

  • The adaptive EMD technique offers an effective solution for ECG noise removal.
  • This method enhances signal quality, leading to improved diagnostic functions in automated systems.
  • The approach is compatible with existing ECG noise reduction techniques.