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Wavelet-Based Arrhythmia Detection in Medical Diagnostics Sensor Networks

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Recent Research in Control Engineering and Decision Making (ICIT 2019)

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 199))

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Abstract

This paper describes an application of wavelet transform for non-equidistant time series analysis in distributed sensor networks. Based on an idea of modern technologies of the Internet of Things and Big Data implementation in digital medicine there is outlined a problem of uneven time series analysis specific for medical diagnostics, specifically electrocardiogram (ECG) monitoring. As a solution there is proposed an original approach and algorithms of calculating the wavelet transform coefficients, using only those samples of the time series that are contained in the width of the wavelet. The advantage of this approach is that the result of the transformation is an even representation. The velocity of the algorithm is improved by taking into account the effective radius of the mother wavelet and calculating its width. The method and software tool for wavelet-based analysis of ECG signals are proposed for arrhythmia detection task. Experimental results show that proposed wavelet-based method of ECG analysis can detect signs of arrhythmia. Results of wireless channel speed test confirm that the chosen hardware meets the requirements of wireless protocol bandwidth. Proposed solutions are suitable for portable heart monitoring systems.

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Author information

Authors and Affiliations

  1. Samara National Research University, 34, Moskovskoye Shosse, Samara, 443086, Russia

    Anastasya Stolbova & Sergey Prokhorov

  2. Penza State University, 40, Krasnaya Street, Penza, 440026, Russia

    Andrey Kuzmin

  3. Samara State Technical University, 244, Molodogvardeyskaya Street, Samara, 443100, Russia

    Anton Ivaschenko

Authors
  1. Anastasya Stolbova
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  2. Sergey Prokhorov
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  3. Andrey Kuzmin
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  4. Anton Ivaschenko
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Corresponding author

Correspondence to Andrey Kuzmin .

Editor information

Editors and Affiliations

  1. Institute of Applied Information Technologies and Communication, Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Olga Dolinina

  2. Institute of Applied Information Technologies and Communication, Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Alexander Brovko

  3. Institute of Applied Information Technologies and Communication, Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Vitaly Pechenkin

  4. Institute of Applied Information Technologies and Communication, Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Alexey Lvov

  5. Department of Automation, Novosibirsk State Technical University, Novosibirsk, Russia

    Vadim Zhmud

  6. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Stolbova, A., Prokhorov, S., Kuzmin, A., Ivaschenko, A. (2019). Wavelet-Based Arrhythmia Detection in Medical Diagnostics Sensor Networks. In: Dolinina, O., Brovko, A., Pechenkin, V., Lvov, A., Zhmud, V., Kreinovich, V. (eds) Recent Research in Control Engineering and Decision Making. ICIT 2019. Studies in Systems, Decision and Control, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-12072-6_38

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