A method for continuously assessing the autonomic response to music-induced emotions through HRV analysis

  • Michele Orini
  • Raquel Bailón
  • Ronny Enk
  • Stefan Koelsch
  • Luca Mainardi
  • Pablo Laguna
Original Article

Abstract

Interest in therapeutic applications of music has recently increased, as well as the effort to understand the relationship between music features and physiological patterns. In this study, we present a methodology for characterizing music-induced effects on the dynamics of the heart rate modulation. It consists of three steps: (i) the smoothed pseudo Wigner-Ville distribution is performed to obtain a time–frequency representation of HRV; (ii) a parametric decomposition is used to robustly estimate the time-course of spectral parameters; and (iii) statistical population analysis is used to continuously assess whether different acoustic stimuli provoke different dynamic responses. Seventy-five healthy subjects were repetitively exposed to pleasant music, sequences of Shepard tones with the same tempo as the pleasant music and unpleasant sounds overlaid with the same sequences of Shepard tones. Results show that the modification of HRV parameters are characterized by an early fast transient phase (15–20 s), followed by an almost stationary period. All kinds of stimuli provoked significant changes compared to the resting condition, while during listening to pleasant music the heart and respiratory rates were higher (for more than 80% of the duration of the stimuli, p < 10−5) and the power of the HF modulation was lower (for more than 70% of the duration of the stimuli, p < 0.05) than during listening to unpleasant stimuli.

Keywords

Heart rate Heart rate variability (HRV) Music Time–frequency analysis Wigner-Ville distribution 

References

  1. 1.
    Bailón R, Mainardi LT, Laguna P (2006) Time–frequency analysis of heart rate variability during stress testing using a priori information of respiratory frequency. In: Proceedings of computers in cardiology, pp 169–172Google Scholar
  2. 2.
    Bailón R, Sörnmo L, Laguna P (2006) A robust method for ECG-based estimation of the respiratory frequency during stress testing. IEEE Trans Biomed Eng 53(7):1273–1285CrossRefGoogle Scholar
  3. 3.
    Bailón R, Laguna P, Mainardi L, Sörnmo L (2007) Analysis of heart rate variability using time-varying frequency bands based on respiratory frequency. In: Proceedings of the 29th international conference of the IEEE engineering in medicine and biology society. IEEE-EMBS Society, Lyon, pp 6674−6677Google Scholar
  4. 4.
    Baraniuk RG, Jones DL (1993) A signal-dependent time–frequency representation: optimal kernel design. IEEE Trans Signal Process 41(4):1589–1602MATHCrossRefGoogle Scholar
  5. 5.
    Baselli G, Porta A, Ferrari G (1995) Models for the analysis of cardiovascular variability signals. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publishing Company, Armonk, pp 135–145Google Scholar
  6. 6.
    Bernardi L, Porta C, Sleight P (2006) Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart 92(4):445–452CrossRefGoogle Scholar
  7. 7.
    Bernardi L, Porta C, Casucci G, Balsamo R, Bernardi NF, Fogari R, Sleight P (2009) Dynamic interactions between musical, cardiovascular, and cerebral rhythms in humans. Circulation 119(25):3171–3180CrossRefGoogle Scholar
  8. 8.
    Bradley MM, Lang PJ (2000) Affective reactions to acoustic stimuli. Psychophysiology 37(2):204–215CrossRefGoogle Scholar
  9. 9.
    Costa A, Boudreau-Bartels G (1995) Design of time–frequency representations using a multiform, tiltable exponential kernel. IEEE Trans Signal Process 43:2283–2301 Google Scholar
  10. 10.
    Flandrin P (1999) Time–frequency/time-scale analysis. Academic Press, San DiegoGoogle Scholar
  11. 11.
    Gomez P, Danuser B (2004) Affective and physiological responses to environmental noises and music. Int J Psychophysiol 53(2):91–103CrossRefGoogle Scholar
  12. 12.
    Gomez P, Danuser B (2007) Relationships between musical structure and psychophysiological measures of emotion. Emotion 7(2):377–387CrossRefGoogle Scholar
  13. 13.
    Goren Y, Davrath LR, Pinhas I, Toledo E, Akselrod S (2006) Individual time-dependent spectral boundaries for improved accuracy in time–frequency analysis of heart rate variability. IEEE Trans Biomed Eng 53(1):35–42CrossRefGoogle Scholar
  14. 14.
    Grossman P, Taylor EW (2007) Toward understanding respiratory sinus arrhythmia: relations to cardiac vagal tone, evolution and biobehavioral functions. Biol Psychol 74(2):263–285CrossRefGoogle Scholar
  15. 15.
    Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time–frequency signal representations. IEEE Signal Process Mag 9(2):21–67CrossRefGoogle Scholar
  16. 16.
    Hlawatsch F, Flandrin P (1997) The interference structure of the Wigner distribution and related time–frequency signal representations. In: The Wigner Distribution—theory and applications in signal processing. Elsevier, Amsterdam, pp 59–113Google Scholar
  17. 17.
    Iwanaga M, Kobayashi A, Kawasaki C (2005) Heart rate variability with repetitive exposure to music. Biol Psychol 70(1):61–66CrossRefGoogle Scholar
  18. 18.
    Jasson S, Médigue C, Maison-Blanche P, Montano N, Meyer L, Vermeiren C, Mansier P, Coumel P, Malliani A, Swynghedauw B (1997) Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time–frequency-domain method. Circulation 96(10):3521–3526Google Scholar
  19. 19.
    Keissar K, Davrath LR, Akselrod S (2009) Coherence analysis between respiration and heart rate variability using continuous wavelet transform. Philos Trans R Soc A 367(1892):1393–1406CrossRefGoogle Scholar
  20. 20.
    Kumaresan R, Tufts D (1982) Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise. IEEE Trans Acoust Speech Signal Process 30(6):833–840CrossRefGoogle Scholar
  21. 21.
    Mainardi LT (2009) On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods. Philos Trans Ser A 367(1887):255–275MATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Mainardi L, Bianchi A, Cerutti S (2002) time–frequency and time-varying analysis for assessing the dynamic responses of cardiovascular control. Critl Rev Biomed Eng 30(1–2):181–223Google Scholar
  23. 23.
    Mainardi L, Montano N, Cerutti S (2004) Automatic decomposition of Wigner distribution and its application to heart rate variability. Methods Inf Med 43:17–21Google Scholar
  24. 24.
    Malik M, Bigger J, Camm A, Kleiger R, Malliani A, Moss A, Schwartz P (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17(3):354Google Scholar
  25. 25.
    Mateo J, Laguna P (2003) Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng 50:334–343CrossRefGoogle Scholar
  26. 26.
    Novak P, Novak V (1993) Time/frequency mapping of the heart rate, blood pressure and respiratory signals. Med Biol Eng Comput 31(2):103–110CrossRefGoogle Scholar
  27. 27.
    Nyklícek I, Thayer J, Van Doornen L (1997) Cardiorespiratory differentiation of musically-induced emotions. J Psychophysiol 11(4):304–321Google Scholar
  28. 28.
    Oldfield RC (1971) The assessment and analysis of handedness: the edinburgh inventory. Neuropsychologia 9(1):97–113CrossRefGoogle Scholar
  29. 29.
    Orini M, Bailón R, Laguna P, Mainardi LT (2007) Modeling and estimation of time-varying heart rate variability during stress test by parametric and non parametric analysis. In: Proceedings of computers in cardiology, pp 29–32Google Scholar
  30. 30.
    Orini M, Bailón R, Mainardi L, Mincholé A, Laguna P (2009) Continuous quantification of spectral coherence using quadratic time–frequency distributions: error analysis and application. In: International conference on computers in cardiologyGoogle Scholar
  31. 31.
    Rajendra Acharya U, Paul Joseph K, Kannathal N, Lim C, Suri J (2006) Heart rate variability: a review. Med Biol Eng Comput 44(12):1031–1051CrossRefGoogle Scholar
  32. 32.
    Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44(2):293–304CrossRefGoogle Scholar
  33. 33.
    Shepard RN (1964) Circularity in judgments of relative pitch. J Acoust Soc Am 36(12):2346–2353CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2010

Authors and Affiliations

  • Michele Orini
    • 1
    • 2
    • 3
  • Raquel Bailón
    • 1
    • 2
  • Ronny Enk
    • 4
    • 5
  • Stefan Koelsch
    • 4
    • 6
  • Luca Mainardi
    • 3
  • Pablo Laguna
    • 1
    • 2
  1. 1.Communication Technology Group, Aragón Institute of Engineering ResearchUniversity of ZaragozaZaragozaSpain
  2. 2.CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)ZaragozaSpain
  3. 3.Department of BioengineeringPolitecnico di MilanoMilanoItaly
  4. 4.Languages of Emotion, Cluster of Excellence der Freien Universität BerlinBerlinGermany
  5. 5.Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
  6. 6.Department of PsychologyUniversity of SussexBrightonUK

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