research-article

Automated Dynamic Electrocardiogram Noise Reduction Using Multilayer LSTM Network

Authors:

Guoqi XieAuthors Info & Claims

MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Pages 197 - 206

https://doi.org/10.1145/3286978.3286988

Published: 05 November 2018 Publication History

Abstract

With the development of Internet of Things, the Healthcare Industrial IoT has become an effective way to curb the high mortality rate of heart disease. The accuracy of such system is mainly rely on the quality of ECG signals, in which noise reduction has been widely used. However, in the IoT environment, many kinds of noise which cannot be predicted in advance exist in signals, and make the signal morphology seriously damaged, which brings great challenge to the existing de-noise methods. By considering the self-adaptation and self-learning of deep neural network, we have proposed a multilayer LSTM model to the noise reduction of dynamic ECGs. Unlike other methods, our model makes both noise and ECG signals as part of time-series data, while other methods always consider them separately. Benefit from the recurrent structure of LSTM model, the most representative features will be extracted in LSTM memory units. By stacking multiple layers per time step, the useful information will be continuously refined and the noise signal will be discarded. Even if the ECG signals comprise many kinds of noise simultaneously, the model can still restore ECG signals with high quality without relying on threshold or signal quality. The experimental results show that the proposed model is insensitive to noise and the improvement of signal-to-noise ratio up to 55dB. This result is much better than the existing methods, which indicates LSTM is a new competitive method for ECG noise reduction.

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Cited By

Wang BHettiarachchi CSuominen HDaskalaki E(2023)Comparative Assessment of Machine Learning Strategies for Electrocardiogram DenoisingAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_40(495-506)Online publication date: 27-Nov-2023
https://doi.org/10.1007/978-981-99-8388-9_40
Lee JOh KKim BYoo S(2020)Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial NetworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2019.293658324:5(1265-1275)Online publication date: May-2020
https://doi.org/10.1109/JBHI.2019.2936583

Index Terms

Automated Dynamic Electrocardiogram Noise Reduction Using Multilayer LSTM Network
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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cover image ACM Other conferences

MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

November 2018

490 pages

ISBN:9781450360937

DOI:10.1145/3286978

General Chairs:
Henning Schulzrinne
Columbia University, USA
,
Pan Li
Case Western Reserve University, USA

Copyright © 2018 ACM.

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Published: 05 November 2018

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MobiQuitous '18

MobiQuitous '18: Computing, Networking and Services

November 5 - 7, 2018

NY, New York, USA

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Cited By

Wang BHettiarachchi CSuominen HDaskalaki E(2023)Comparative Assessment of Machine Learning Strategies for Electrocardiogram DenoisingAI 2023: Advances in Artificial Intelligence10.1007/978-981-99-8388-9_40(495-506)Online publication date: 27-Nov-2023
https://doi.org/10.1007/978-981-99-8388-9_40
Lee JOh KKim BYoo S(2020)Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial NetworkIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2019.293658324:5(1265-1275)Online publication date: May-2020
https://doi.org/10.1109/JBHI.2019.2936583

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