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Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional

Aditya Pal1, Hari Mohan Rai2, Saurabh Agarwal3

  • 1Department of Information Technology, Dronacharya Group of Institutions, Greater Noida 201306, India.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

This study introduces a hybrid denoising method for electrocardiogram (ECG) signals, significantly improving cardiovascular diagnosis accuracy. The enhanced signal processing boosts classification performance for critical cardiac care applications.

Keywords:
ECG signal classificationbiomedical signal processingcardiac health monitoringdenoising techniquesmodified lightweight MLCNNnoise reduction in ECG

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Electrocardiogram (ECG) signal classification is vital for diagnosing cardiac conditions.
  • Signal acquisition can introduce noise, impacting diagnostic accuracy.
  • Effective denoising is crucial for reliable ECG analysis.

Purpose of the Study:

  • To enhance ECG signal classification accuracy by employing and evaluating various denoising techniques.
  • To improve the reliability of cardiovascular diagnoses through advanced signal processing.
  • To introduce a novel hybrid denoising method for ECG signals.

Main Methods:

  • Simulated realistic noise (Gaussian, salt and pepper, speckle, uniform, exponential) in ECG data.
  • Applied denoising methods: wavelet transform, median filter, Gaussian filter, and a hybrid wavelet-median filter.
  • Utilized a modified lightweight Convolutional Neural Network (CNN) or MLCNN for signal classification.

Main Results:

  • The hybrid wavelet-median filter demonstrated superior performance over individual methods, evidenced by low Mean Squared Error (MSE) of 0.0012 and Mean Absolute Error (MAE) of 0.025.
  • High R-squared (0.98) and Pearson correlation coefficient (0.99) confirmed the hybrid method's effectiveness in preserving ECG characteristics.
  • Classification using denoised data achieved significantly higher accuracy (0.92), precision (0.91), recall (0.90), and F1-score (0.91) compared to noisy data (0.80, 0.78, 0.82, 0.80 respectively).

Conclusions:

  • The proposed hybrid denoising technique effectively removes noise while preserving essential ECG signal features.
  • Improved ECG signal quality through denoising directly enhances the accuracy of cardiac diagnoses.
  • This study underscores the importance of signal preprocessing for robust real-time ECG analysis in clinical settings.