Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning
Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In par...
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| Format: | Article |
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Kerman University of Medical Sciences
2020-12-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
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| Online Access: | http://jhbmi.ir/article-1-468-en.html |
| _version_ | 1811176360796749824 |
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| author | Saber Fooladi Hassan Farsi Farima Farsi |
| author_facet | Saber Fooladi Hassan Farsi Farima Farsi |
| author_sort | Saber Fooladi |
| collection | DOAJ |
| description | Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by noise due to external sources or other physiological processes in the human body.
Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the noise. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more noise in the ECG signal.
Results: Signal-to-Noise ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove noise. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning.
Conclusion: The results of the simulation showed that the proposed method improved the removal of noise from the ECG signal about 9.56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-noise waveform. |
| first_indexed | 2024-04-10T19:51:14Z |
| format | Article |
| id | doaj.art-f8dbc508935d4e0a856d2d735fe1dc82 |
| institution | Directory Open Access Journal |
| issn | 2423-3870 2423-3498 |
| language | fas |
| last_indexed | 2024-04-10T19:51:14Z |
| publishDate | 2020-12-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | مجله انفورماتیک سلامت و زیست پزشکی |
| spelling | doaj.art-f8dbc508935d4e0a856d2d735fe1dc822023-01-28T10:30:25ZfasKerman University of Medical Sciencesمجله انفورماتیک سلامت و زیست پزشکی2423-38702423-34982020-12-0173318325Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep LearningSaber Fooladi0Hassan Farsi1Farima Farsi2 Ph.D. Student in Electrical Engineering-Telecommunications, Electrical Engineering-Telecommunications Dept., Faculty of Electrical Engineering and Computer, Shokatabad Campus, University of Birjand, Birjand, Iran Ph.D. in Electrical Engineering-Telecommunications, Professor, Electrical Engineering-Telecommunications Dept., Faculty of Electrical Engineering and Computer, Shokatabad Campus, University of Birjand, Birjand, Iran Medical Doctoral Student, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by noise due to external sources or other physiological processes in the human body. Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the noise. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more noise in the ECG signal. Results: Signal-to-Noise ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove noise. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning. Conclusion: The results of the simulation showed that the proposed method improved the removal of noise from the ECG signal about 9.56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-noise waveform.http://jhbmi.ir/article-1-468-en.htmlecg signalwavelet transformdeep learning |
| spellingShingle | Saber Fooladi Hassan Farsi Farima Farsi Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning مجله انفورماتیک سلامت و زیست پزشکی ecg signal wavelet transform deep learning |
| title | Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning |
| title_full | Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning |
| title_fullStr | Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning |
| title_full_unstemmed | Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning |
| title_short | Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning |
| title_sort | adaptive filtering strategy to remove noise from ecg signals using wavelet transform and deep learning |
| topic | ecg signal wavelet transform deep learning |
| url | http://jhbmi.ir/article-1-468-en.html |
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