ECG Noise Removal Using FCN DAE Method

An electrocardiogram (ECG) is a straightforward test that measures your heart rate and electrical activity. Electrical signals produced by your heart are detected by skin-connected nerves each time it beats. ECG signals are susceptible to noise contamination in real-world conditions, which can lead...

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Published in:2022 2nd International Conference on Intelligent Technologies (CONIT) pp. 1 - 8
Main Authors: Kollem, Sreedhar, Baig, Mirza Rahman, Lasya, Donthireddy, Kalyan, Eligeti Ashwad, Varma, Karre Nithin
Format: Conference Proceeding
Language:English
Published: IEEE 24.06.2022
Subjects:
Convolution
ECG
Electric variables measurement
Electrocardiography
FCN (Fully Convolutional Network) DAE (Denoising Autoencoders)
Heart rate
Interference
Noise reduction
Recycling
SNR(Signal-to-Noise Ratio)
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Abstract An electrocardiogram (ECG) is a straightforward test that measures your heart rate and electrical activity. Electrical signals produced by your heart are detected by skin-connected nerves each time it beats. ECG signals are susceptible to noise contamination in real-world conditions, which can lead to misunderstanding. Baseline wanders and power line interference are the two main sources of noise in the ECG signal. To tackle these problems and eliminate inaccuracies, special emphasis has been dedicated to interpreting the ECG in order to achieve a precise diagnosis and analysis. To recycle pure data in its audio version, a denoising autoencoder (DAE) might be utilized. The results of experiments on ECG signals with various degrees of SNR input reveal that FCN outperforms fully connected neural network-and convolutional neural-based denoising network models significantly.
AbstractList An electrocardiogram (ECG) is a straightforward test that measures your heart rate and electrical activity. Electrical signals produced by your heart are detected by skin-connected nerves each time it beats. ECG signals are susceptible to noise contamination in real-world conditions, which can lead to misunderstanding. Baseline wanders and power line interference are the two main sources of noise in the ECG signal. To tackle these problems and eliminate inaccuracies, special emphasis has been dedicated to interpreting the ECG in order to achieve a precise diagnosis and analysis. To recycle pure data in its audio version, a denoising autoencoder (DAE) might be utilized. The results of experiments on ECG signals with various degrees of SNR input reveal that FCN outperforms fully connected neural network-and convolutional neural-based denoising network models significantly.
Author Kalyan, Eligeti Ashwad
Varma, Karre Nithin
Baig, Mirza Rahman
Kollem, Sreedhar
Lasya, Donthireddy
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  givenname: Eligeti Ashwad
  surname: Kalyan
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  givenname: Karre Nithin
  surname: Varma
  fullname: Varma, Karre Nithin
  email: karrenithinvarma123@gmail.com
  organization: SR Engineering College,Dept. of ECE,Warangal,India
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Snippet An electrocardiogram (ECG) is a straightforward test that measures your heart rate and electrical activity. Electrical signals produced by your heart are...
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SubjectTerms Convolution
ECG
Electric variables measurement
Electrocardiography
FCN (Fully Convolutional Network) DAE (Denoising Autoencoders)
Heart rate
Interference
Noise reduction
Recycling
SNR(Signal-to-Noise Ratio)
Title ECG Noise Removal Using FCN DAE Method
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