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  3. A Denoising And Fourier Transformation-based Spectrograms In Ecg Classification Using Convolutional Neural Network.
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  3. A Denoising And Fourier Transformation-based Spectrograms In Ecg Classification Using Convolutional Neural Network.

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A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

Muhammad Farhan Safdar1, Robert Marek Nowak1, Piotr Pałka1

  • 1Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.

Sensors (Basel, Switzerland)
|December 23, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

Spectrograms, not raw electrocardiogram (ECG) signals, improve cardiac disease diagnosis accuracy using convolutional neural networks (CNNs). This novel approach enhances efficiency and reduces computational demands for reliable heart condition assessment.

Keywords:
Fourier transformationconvolutional neural networkelectrocardiogramspectrograms

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Signal Processing

Background:

  • Electrocardiogram (ECG) signals are vital for assessing heart conditions and diagnosing cardiac diseases.
  • Traditional ECG interpretation is complex, time-consuming, and requires specialized expertise due to large data volumes.
  • Advancements in neural networks show promise for efficient biomedical signal interpretation, including ECG and electroencephalogram (EEG).

Purpose of the Study:

  • To investigate the efficacy of using spectrograms, derived from ECG signals, for improved cardiac disease classification.
  • To develop a simplified Convolutional Neural Network (CNN) architecture for accurate and computationally efficient ECG analysis.
  • To compare the diagnostic performance of spectrogram-based ECG analysis against traditional raw signal analysis.

Main Methods:

  • ECG data from the PTB-XL dataset were processed into both raw signal and spectrogram formats.
  • Spectrograms were generated using Short-Time Fourier Transformation (STFT) and data reduction via frequency filtration.
  • A simplified CNN model was trained and evaluated on both raw signal and spectrogram datasets for binary classification.

Main Results:

  • The proposed approach utilizing spectrograms achieved a highest accuracy of 99.06% in ECG classification.
  • Spectrogram-based analysis demonstrated superior performance compared to raw ECG signal analysis.
  • The method reduced memory usage and computational power requirements by employing a simpler CNN architecture.

Conclusions:

  • Spectrograms offer a more effective representation of ECG data for cardiac disease classification than raw signals.
  • The developed CNN approach provides a computationally efficient and highly accurate method for ECG interpretation.
  • This technique holds potential for improving the accessibility and speed of cardiac diagnostics.