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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Performance Science
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1403599

A framework for modelling performers' beat-to-beat heart intervals using music features and Interpretation Maps

Provisionally accepted
Mateusz Soliński Mateusz Soliński 1,2*Courtney N. Reed Courtney N. Reed 1,2Elaine Chew Elaine Chew 1,2
  • 1 School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London, London, England, United Kingdom
  • 2 Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King's College London, London, England, United Kingdom

The final, formatted version of the article will be published soon.

    Objective: Music strongly modulates our autonomic nervous system. This modulation is evident in musicians' beat-to-beat heart (RR) intervals, a marker of heart rate variability (HRV), and can be related to music features and structures. We present a novel approach to modelling musicians' RR interval variations, analysing detailed components within a music piece to extract continuous music features and annotations of musicians' performance decisions.Methods: A professional ensemble (violinist, cellist, pianist) performs Schubert's Trio No. 2, Op. 100, Andante con moto nine times during rehearsals. RR interval series are collected from each musician using wireless ECG sensors. Linear mixed models are used to predict their RR intervals based on music features (tempo, loudness, note density), interpretive choices (Interpretation Map), and a starting factor.The models explain approximately half of the variability of the RR interval series for all musicians, with R-squared = 0.606 (violinist), 0.494 (cellist), and 0.540 (pianist). The features with the strongest predictive values were loudness, climax, moment of concern, and starting factor.The method revealed the relative effects of different music features on autonomic response. For the first time, we show a strong link between an interpretation map and RR interval changes. Modelling autonomic response to music stimuli is important for developing medical and non-medical interventions. Our models can serve as a framework for estimating performers' physiological reactions using only music information that could also apply to listeners.

    Keywords: RR intervals, Heart rate variability, cardiac modelling, music performance, interpretation map, Music Features

    Received: 20 Mar 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Soliński, Reed and Chew. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mateusz Soliński, School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London, London, WC2R 2LS, England, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.