ECG Signal Denoising Using 1D Convolutional Neural Network

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical...

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Bibliographic Details
Published in:Computer engineering and applications journal Vol. 13; no. 2; pp. 60 - 68
Main Authors: Rifai, Ahmad, Rachmamtullah, Muhammad Naufal, Sari, Winda Kurnia
Format: Journal Article
Language:English
Published: Palembang Computer Engineering and Applications Journal, Universitas Sriwijaya 01.06.2024
Subjects:
Artificial neural networks
Cardiac stress tests
Diagnosis
Electrocardiography
Neural networks
Noise levels
Noise monitoring
Noise reduction
Performance evaluation
Power lines
Signal quality
Signal reconstruction
Signal to noise ratio
ISSN:2252-4274, 2252-5459
Online Access:Get full text
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Summary:Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosis
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ISSN:2252-4274
2252-5459
DOI:10.18495/comengapp.v13i2.482