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Influence of Music on HRV Indices Derived from ECG and SCG

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Innovations in Biomedical Engineering (AAB 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1223))

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Abstract

Music surrounds us every day and fulfills many functions. Its influence on human physiology has been widely described in the literature. In order to evaluate quantitatively this influence, we used heart rate variability (HRV) analysis on signals acquired before listening to the music and after listening to the music for 50  min. The following HRV indices were considered: AVNN, SDNN, RMSSD, pNN50, PLF, PHF, LF/HF, \(SD_1\), \(SD_2\), EA, VLI, and VAI. We observed no statistically significant differences between HRV indices calculated on signals registered before and after 50 min of listening to the music, although we observed the increase of PLF.

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Authors and Affiliations

  1. Faculty of Biomedical Engineering, Department of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Roosevelta 40, 41-800, Zabrze, Poland

    Szymon Sieciński & Paweł Kostka

Authors
  1. Szymon Sieciński
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  2. Paweł Kostka
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Corresponding author

Correspondence to Szymon Sieciński .

Editor information

Editors and Affiliations

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

    Marek Gzik

  2. Faculty of Biomedical Engineering/Department of Biomaterials and Medical Devices Engineering, Silesian University of Technology, Zabrze, Poland

    Zbigniew Paszenda

  3. Faculty of Biomedical Engineering/Department of Informatics and Medical Devices, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  4. Faculty of Biomedical Engineering/Department of Biosensors and Processing of Biomedical Signals, Silesian University of Technology, Zabrze, Poland

    Ewaryst Tkacz

  5. American Heart of Poland, Katowice, Poland

    Krzysztof Milewski

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Sieciński, S., Kostka, P. (2021). Influence of Music on HRV Indices Derived from ECG and SCG. In: Gzik, M., Paszenda, Z., Pietka, E., Tkacz, E., Milewski, K. (eds) Innovations in Biomedical Engineering. AAB 2020. Advances in Intelligent Systems and Computing, vol 1223. Springer, Cham. https://doi.org/10.1007/978-3-030-52180-6_39

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