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Pointwise Estimation of Noise in a Multilead ECG Record

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Information Technology in Biomedicine (ITIB 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1011))

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Abstract

Noise of certain characteristics can efficiently be extracted from a multilead physiological record. This paper presents a novel method for distilling random components of electrocardiogram. First, the algorithm calculates first principal component of the record as a noise-reduced reference. Next, a multiplication-addition network is trained and used for prediction of the value in each given lead based on remaining leads. Finally, the time series of prediction errors are decorrelated with the noise-reduced reference, what removes the geometry-related component of unexpectedness. In tests with poor electrode contact and muscle fibrillation noise patterns, the method shows static noise estimation accuracy of 80.3 % and 84 % respectively and in dynamic tests - the accuracy of 78.1% and 82.6%, what outperforms other known methods.

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Acknowledgement

This scientific work is supported by the AGH University of Science and Technology in year 2019 from the subvention granted by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Piotr Augustyniak

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  1. Piotr Augustyniak
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Correspondence to Piotr Augustyniak .

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Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

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Augustyniak, P. (2019). Pointwise Estimation of Noise in a Multilead ECG Record. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_41

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