Effects of Algorithmic Music on the Cardiovascular Neural Control

Abstract

Music influences many physiological parameters, including some cardiovascular (CV) control indices. The complexity and heterogeneity of musical stimuli, the integrated response within the brain and the limited availability of quantitative methods for non-invasive assessment of the autonomic function are the main reasons for the scarcity of studies about the impact of music on CV control. This study aims to investigate the effects of listening to algorithmic music on the CV regulation of healthy subjects by means of the spectral analysis of heart period, approximated as the time distance between two consecutive R-wave peaks (RR), and systolic arterial pressure (SAP) variability. We studied 10 healthy volunteers (age 39 ± 6 years, 5 females) both while supine (REST) and during passive orthostatism (TILT). Activating and relaxing algorithmic music tracks were used to produce possible contrasting effects. At baseline, the group featured normal indices of CV sympathovagal modulation both at REST and during TILT. Compared to baseline, at REST, listening to both musical stimuli did not affect time and frequency domain markers of both SAP and RR, except for a significant increase in mean RR. A physiological TILT response was maintained while listening to both musical tracks in terms of time and frequency domain markers, compared to baseline, an increase in mean RR was again observed. In healthy subjects featuring a normal CV neural profile at baseline, algorithmic music reduced the heart rate, a potentially favorable effect. The innovative music approach of this study encourages further research, as in the presence of several diseases, such as ischemic heart disease, hypertension, and heart failure, a standardized musical stimulation could play a therapeutic role.

Keywords: Melomics-Health; algorithmic music; arterial pressure variability; baroreflex; cardiovascular neural control; heart rate variability; music listening.

Figure 1

Track1, activating algorithmic music; Track2, relaxing algorithmic music; Rand., randomized; REST, supine condition; TILT, passive head-up tilting test; min., minutes; REC, Recovery Time.

Figure 2

Example of RR (upper panels) and SAP (lower panels) series for each studied condition (B, baseline on the left, Track1 in the middle and Track2 on the right) during resting (REST, line 1 and 3 from the top) and tilting (TILT, line 2 and 4 from the top).

Figure 3

Results of the RR variability analysis in the healthy subjects while supine (REST) and during 70° head-up tilt test (TILT) in normal condition (B) and while listening activating (Track1) and relaxing (Track2) algorithmic music. RR, RR interval; μ_RR, RR mean (a); σ²_RR, RR variance (b); LF, low frequency; LF_a,RR, absolute power of RR in the LF band (c); LF_nu,RR, normalized power of RR in the LF band (d); HF, high frequency; HF_a,RR, absolute power of RR in the HF band (e); HF_nu,RR, normalized power of RR in the HF band (f). Data are expressed as mean ± standard deviation. § indicates p < 0.05 REST vs. TILT. * Indicates B vs. Track1 and vs. Track2.

Figure 4

Results of the respiratory rate estimation in the healthy subjects while supine (REST) and during 70° head-up tilt test (TILT) in normal condition (B) and while listening to Track1 (activating algorithmic music) and Track2 (relaxing algorithmic music). Data are expressed as mean ± standard deviation.

Figure 5

Results of the cardiac baroreflex sensitivity (cBRS) estimation in the healthy subjects while supine (REST) and during 70° head-up tilt test (TILT) in normal condition (B) and while listening to activating (Track1) and relaxing (Track2) algorithmic music. Data are expressed as mean ± standard deviation. § indicates p < 0.05 REST vs. TILT.