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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Cardiac Severity Classification Using Pre Trained Neural Networks.

Pinjala N Malleswari1, Ch Hima Bindu2, K Satya Prasad3

  • 1University College of Engineering, JNTUK, Kakinada, AP, India. pinjalamalleswari@gmail.com.

Interdisciplinary Sciences, Computational Life Sciences
|January 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid method combining Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) for enhanced electrocardiogram (ECG) analysis. The proposed technique significantly improves the accuracy of classifying cardiac conditions using neural networks.

Keywords:
AccuracyECGECG classificationEMD-DWTFFBPNNMIT-BIH database

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) is crucial for diagnosing heart disease.
  • Accurate feature extraction from ECG signals is essential for effective diagnosis.
  • Existing methods require improvement in precision and efficiency for cardiac condition classification.

Purpose of the Study:

  • To propose a hybrid approach for classifying cardiac conditions using ECG signals.
  • To enhance ECG signal pre-processing by combining Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT).
  • To evaluate the classification accuracy of the proposed method using a Feed Forward Back Propagation Neural Network (FFBPNN).

Main Methods:

  • ECG signals were pre-processed using a combination of EMD and DWT.
  • Empirical Mode Decomposition (EMD) decomposed the signal into Intrinsic Mode Functions (IMFs).
  • The first three IMFs were combined, and DWT was applied for denoising, followed by classification with FFBPNN.

Main Results:

  • The proposed hybrid EMD-DWT method achieved a classification accuracy of 95.0797%.
  • This accuracy surpasses individual EMD (67.0762%) and DWT (90.4305%) approaches.
  • The methodology demonstrated superior performance on the MIT-BIH database compared to other methods.

Conclusions:

  • The hybrid EMD-DWT approach offers a significant improvement in ECG signal processing for cardiac condition classification.
  • This method provides higher accuracy and efficiency than traditional techniques.
  • The proposed technique holds promise for more precise diagnosis of heart diseases.