Machine learning based techniques for ECG noise removal and feature extraction

Research Article
Open access

Machine learning based techniques for ECG noise removal and feature extraction

Xiaohan Gu 1*
  • 1 Hong Kong Baptist University    
  • *corresponding author 21253250@life.hkbu.edu.hk
Published on 20 December 2023 | https://doi.org/10.54254/2753-8818/23/20231053
TNS Vol.23
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-219-0
ISBN (Online): 978-1-83558-220-6

Abstract

Machine learning (ML) is being applied to all aspects of life with the development of artificial intelligence (AI). This paper explores the application of machine learning technology in electrocardiogram (ECG) analysis to diagnose and classify a patient's current cardiac disease, predict possible future diseases, and provide a personalised treatment plan. However, several challenges have been highlighted. First, individual ECG signals display variability, causing concern about effective diagnosis based on changing ECG data. Second, different diseases can produce similar ECG results, requiring powerful classification algorithms to accurately classify diseases. Finally, using patient information to predict the probability of future heart attacks is critical to developing appropriate prevention and treatment strategies. Overcoming these challenges could revolutionise the field of cardiology. It could enable precise and proactive medical intervention. The study highlights the potential of machine learning to improve cardiovascular care and personalised medicine and emphasises the importance of addressing key challenges to maximise its impact in clinical practice.

Keywords:

machine learning, electrocardiogram, healthcare

Gu,X. (2023). Machine learning based techniques for ECG noise removal and feature extraction. Theoretical and Natural Science,23,200-203.
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References

[1]. F. Censi et al., "Effect of ECG filtering on time domain analysis of the P-wave," 2008 Computers in Cardiology, Bologna, Italy, 2008, pp. 1077-1080, doi: 10.1109/CIC. 2008.4749232.

[2]. Mneimneh, M., Yaz, E., Johnson, M., & Povinelli, R. J. (2006). An adaptive kalman filter for removing baseline wandering in ECG signals. Computing in Cardiology Conference, 253–256. http://cinc.mit.edu/archives/2006/pdf/0253.pdf

[3]. Mahesh S Chavan, R A Agrawala, M.D. Uplane. " Design and implementation of Digital FIR Equiripple Notch Filter on ECG Signal for removal of Power line Interference" 4th Wseas International Conference on Electronics, Control & Signal Processing Miami Florida USA 17-19 Nov. 2005 (pp 58-63).

[4]. Sharma, N., & Sidhu, J. S. (2016). Removal of noise from ecg signal using adaptive filtering. Indian Journal of Science and Technology, 9(48).

[5]. Ramadevi, G. N., Rani, K. U., & Lavanya, D. (2015). Importance of feature extraction for classification of breast cancer datasets—a study. International Journal of Scientific and Innovative Mathematical Research, 3(2), 763-368.

[6]. Gosling, R., & Budge, M. (2003). Terminology for describing the elastic behavior of arteries. Hypertension, 41(6), 1180–1182. https://doi.org/10.1161/01.hyp.0000072271.36866.2a

[7]. Giuliani, A. (2017). The application of principal component analysis to drug discovery and biomedical data. Drug Discovery Today, 22(7), 1069–1076. https://doi.org/10.1016/ j.drudis.2017.01.005

Cite this article

Gu,X. (2023). Machine learning based techniques for ECG noise removal and feature extraction. Theoretical and Natural Science,23,200-203.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Biological Engineering and Medical Science

ISBN：978-1-83558-219-0(Print) / 978-1-83558-220-6(Online)
Editor：Alan Wang
Conference website: https://www.icbiomed.org/
Conference date: 2 September 2023
Series: Theoretical and Natural Science
Volume number: Vol.23
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. F. Censi et al., "Effect of ECG filtering on time domain analysis of the P-wave," 2008 Computers in Cardiology, Bologna, Italy, 2008, pp. 1077-1080, doi: 10.1109/CIC. 2008.4749232.

[2]. Mneimneh, M., Yaz, E., Johnson, M., & Povinelli, R. J. (2006). An adaptive kalman filter for removing baseline wandering in ECG signals. Computing in Cardiology Conference, 253–256. http://cinc.mit.edu/archives/2006/pdf/0253.pdf

[3]. Mahesh S Chavan, R A Agrawala, M.D. Uplane. " Design and implementation of Digital FIR Equiripple Notch Filter on ECG Signal for removal of Power line Interference" 4th Wseas International Conference on Electronics, Control & Signal Processing Miami Florida USA 17-19 Nov. 2005 (pp 58-63).

[4]. Sharma, N., & Sidhu, J. S. (2016). Removal of noise from ecg signal using adaptive filtering. Indian Journal of Science and Technology, 9(48).

[5]. Ramadevi, G. N., Rani, K. U., & Lavanya, D. (2015). Importance of feature extraction for classification of breast cancer datasets—a study. International Journal of Scientific and Innovative Mathematical Research, 3(2), 763-368.

[6]. Gosling, R., & Budge, M. (2003). Terminology for describing the elastic behavior of arteries. Hypertension, 41(6), 1180–1182. https://doi.org/10.1161/01.hyp.0000072271.36866.2a

[7]. Giuliani, A. (2017). The application of principal component analysis to drug discovery and biomedical data. Drug Discovery Today, 22(7), 1069–1076. https://doi.org/10.1016/ j.drudis.2017.01.005

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