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Autonomic Modulation During a Cognitive Task Using a Wearable Device

  • Maria Paula Bonomini
  • Mikel Val-CalvoEmail author
  • Alejandro Díaz-Morcillo
  • José Manuel Ferrández Vicente
  • Eduardo Fernández-Jover
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)

Abstract

Heart-brain interaction is by nature bidirectional, and then, it is sensible to expect the heart, via the autonomic nervous system (ANS), to induce changes in the brain. Respiration can originate differentiated ANS states reflected by HRV. In this work, we measured the changes in performance during a cognitive task due to four autonomic states originated by breath control: at normal breathing (NB), fast breathing (FB), slow breathing (SB) and control phases. ANS states were characterized by temporal (SDNN) and spectral (LF and HF power) HRV markers. Cognitive performance was measured by the response time (RT) and the success rate (SR). HRV parameters were acquired with the wristband Empatica E4. Classification was accomplished, firstly, to find the best ANS variables that discriminated the breathing phases (BPH) and secondly, to find whether ANS parameters were associated to changes in RT and SR. In order to compensate for possible bias of the test sets, 1000 classification iterations were run. The ANS parameters that better separated the four BPH were LF and HF power, with changes about 300\(\%\) from controls and an average classification rate of 59.9\(\%\), a 34.9\(\%\) more than random. LF and HF explained RT separation for every BPH pair, and so was HF for SR separation. The best RT classification was 63.88\(\%\) at NB vs SB phases, while SR provided a 73.39\(\%\) at SB vs NB phases. Results suggest that breath control could show a relation with the efficiency of certain cognitive tasks. For this goal the Empatica wristband together with the proposed methodology could help to clarify this hypothesis.

Keywords

ANS HRV Response time Cognition 

Notes

Acknowledgements

We want to acknowledge to Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, from Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maria Paula Bonomini
    • 1
    • 2
  • Mikel Val-Calvo
    • 3
    • 5
    Email author
  • Alejandro Díaz-Morcillo
    • 4
  • José Manuel Ferrández Vicente
    • 5
  • Eduardo Fernández-Jover
    • 6
  1. 1.Instituto Argentino de Matemáticas Alberto CalderónCABAArgentina
  2. 2.Instituto Tecnológico de Buenos Aires (ITBA)CABAArgentina
  3. 3.Dpto. de Inteligencia ArtificialUniversidad Nacional de Educación a Distancia (UNED)MadridSpain
  4. 4.Dpto. Tecnologías de la Información y las ComunicacionesUniv. Politécnica de CartagenaCartagenaSpain
  5. 5.Dpto. Electrónica, Tecnología de Computadoras y ProyectosUniv. Politécnica de CartagenaCartagenaSpain
  6. 6.Instituto de BioingenieríaUniv. Miguel HernándezElcheSpain

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