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The voice of music resounding on HRV.

Harmonious treasure

Heart rate variability (HRV) is defined as the chaotic oscillation in the duration of Qq the R-R interval between each heart beat that occurs as a result of autonomic nervous system (ANS) sympathetic and parasympathetic activities on the sinus node (Eagle 1996; Vinik 2012). It is expressed in normal sinus rhythm on electrocardiogram recordings, ranging from a few minutes to 24 hours (Freeman 2006). A reduction in HRV indicates an inability or attenuation in the autonomic nervous system's or sinoatrial node's responsiveness to change. Research (Hon & Lee 1965) revealed that reduced beat-to-beat variation of the fetal heart was associated with fetal distress before any change in heart rate could be noticed. An association between decreased HRV and increased risk of mortality after acute myocardial infarction was proved (Wolf et al 1977), and it was confirmed that HRV is a powerful and independent predictor of mortality after acute myocardial infarction. (Kleiger et al 1996)

HRV is a noninvasive measurement that can be used to identify phenomena related to ANS in healthy individuals, athletes and patients with diseases (Aubert 2003, Pumprla 2002). Figure 1 shows rate tachogram obtained from the RR intervals of a normal young adult and a normal newborn. It is observed that HRV is much smaller in the newborn. (Vanderlei 2009)

While the concept of "variability" may raise red flags, it is the lack of variability in beat-to-beat HR that is believed to reflect general autonomic dysfunction in individuals with cardiovascular disease, hypertension, diabetes, high cholesterol, multiple sclerosis, who have had an ischemic stroke or myocardial infarction, who are obese, or who smoke, and evidence from several sources suggests that HRV is an independent predictor of all-cause mortality. (Hoshishiba et al 1995)

The two branches of the ANS, sympathetic and parasympathetic nervous system regulate visceral functions in order to maintain the homeostatic milieu of the body and render the body able to react and adapt to external and internal stressor stimuli. A very composite and interconnected regulating mechanism operates at different levels, both central and peripheral, in order to coordinate ANS functions. (Montano et al 2009)

Within physiological conditions, the regulation of several fundamental visceral functions such as cardiovascular, respiratory, and gastrointestinal systems, is based on the reciprocal activation of the two autonomic subsystems, the so-called "sympatho-vagal balance": the activation of one branch, i.e., sympathetic outflow, is associated with a withdrawal of the other, i.e., parasympathetic drive, and vice versa. (Malliani 1998) This mechanism has been considered a key stone paradigm of the ANS function for many years; however, it has been suggested that the co-activation of both sympathetic and parasympathetic systems is not only physiologically possible, but it is also a rule in peculiar situations such as chemoreceptor reflexes (i.e., during apneas), exercise, and cold face immersion. (Koizumi 1982; Paton 2005)

The cardiac excitation begins with an electrical impulse generated in the sinus node (the primary pacemaker of the heart), which is being distributed through right atria, then left atria, resulting in atrial depolarization, which is represented on the electrocardiogram (ECG) by the P wave. The node is populated by millions of cells, each of them being a small oscillator with a natural frequency of its own. This impulse is then conducted to ventricles through the atrioventricular node and distributed by the Purkinje fibers, resulting in the depolarization of the ventricles, which is represented on the ECG by the Q, R and S waves, forming the QRS complex. Ventricular repolarization is represented by the T wave.

In the healthy heart these frequencies are phase-locked, enabling the node to produce regular contraction commands. The sinoatrial node has a natural frequency of about 90 beats per min. The node is affected by the ANS (figure 2). The sympathetic nervous system and the vagal nerve transmit impulses from a cardiopulmonary oscillator consisting of interneuron connecting brainstem nuclei (the nucleus of the solitary tract and the nucleus ambiguus-Vickhoff 2013). In addition, pulmonary stretch receptors are involved in the neural mediation of respiratory sinus arrhythmia (Taha 1995)-see further.

For many years, several techniques have been developed for the assessment of ANS: (a) dosage of plasmatic and urinary catecholamines (Goldstein 1983), which nowadays is not considered as a highly reliable index of sympathetic activity (Montano 2009), (b) muscle sympathetic nerve activity (MSNA), a direct but invasive recording of sympathetic activity using a microneurography technique (Wallin 2007), (c) analysis of HRV, a non-invasive tool able to provide reliable information on the sympathetic and parasympathetic oscillations of the heart period and arterial pressure time series, (d) more recent non-linear approaches based on entropy-derived measures and symbolic analysis of heart period time series. (Tobaldini et al 2009)

The complexity of this dual innervation is redoubled, however, by its connection to an intricate neuroarchitecture with descending, ascending, and bidirectional links between cortical, midbrain, and brainstem structures (Thayer et al 2009)-figure 3. These structures include the orbitofrontal, ventromedial prefrontal, anterior cingulate, and insular cortices, basal ganglia, central nucleus of the amygdala, nucleus of the solitary tract, nucleus ambiguus, and periaqueductal gray matter, among others.

The HRV indexes are obtained by analyzing the intervals between R waves, which can be captured by instruments such as the electrocardiographer, the digital-to-analog converter and the cardio-frequency meter, from external sensors placed at specific points of the body (Rajendra 2006).

Softly, softly they sing their spell

For the HRV analysis, indexes obtained by linear methods, time and frequency domain, and nonlinear methods can be used:

a) linear types

The linear methods are divided into two types: time domain analysis, performed by statistical and geometric indexes, and frequency domain analysis (Vanderlei et al 2009). For the HRV time domain analysis, thus named for expressing the results in unit time (milli-seconds), every normal RR interval (sinus beat) is measured during a determined time interval and, thereafter, based on statistical or geometric methods (mean, standard deviation and histogram-derived indexes or the Cartesian coordinates map of the RR intervals), the translator indexes of fluctuations during the cardiac cycles are calculated (Vanderlei 2009). A largely used possibility to process RR intervals in time domain is from geometrical methods, whereas the triangular index and Lorenz plot (or Poincare Plot) is the best known. Another linear method is the frequency domain, whereas the spectral power density is the most widely used, when it deals with studies on individuals at rest-figure 4 (Vanderlei et al 2009). This plot includes at least three frequency peaks. Fast frequency periodicities (high frequency, HF), in the range 0.15-0.4 Hz, are largely due to the influence of the respiratory phase on the vagal tone. Low-frequency periodicities (LF), in the range of 0.04-0.15 Hz, are produced by baroreflex feedback loops, affected by both sympathetic and parasympathetic modulations of the heart. Very low frequency periodicities (VLF), i.e. less than 0.04 Hz, have been variously ascribed to modulation by chemoreception, thermoregulation and the influence of vasomotor activity, which is related, among others, to the renin-angiotensin-aldosterone system (RAS)-figure 5. (Papaioannou 2013)

The LF component of HRV is probably the most contentious aspect with respect to cardiovascular variability. Two different potential origins are suggested: 1) the central oscillator theory, and 2) the baroreflex feedback loop theory (Lanfranchi 2002).

According to the first theory, it is believed that LF oscillations reflect the sympathetic tone and are generated by the brain stem circuits. In terms of the second theory a change in blood pressure is sensed by arterial baroreceptors, resulting in heart rate adjustment through the central nervous system and via also the fast vagal action and the slower sympathetic action. At the same time, baroreceptors induce a slow sympathetic withdrawal from the vessels. The delay in the sympathetic branch of the baroreflex also determines a new oscillation, which is sensed by the baroreflex and induces a new oscillation in heart rate. (Papaioannou 2013)

b) nonlinear types

The nonlinear behavior is predominant in human systems, because of its dynamic nature complex, which cannot be described properly by linear methods. Chaos theory describes elements manifesting behaviors that are extremely sensitive to initial conditions, and they are difficult to repeat, but nonetheless are deterministic.

The theories of nonlinear systems have been progressively applied to interpret, explain and predict the behavior of biological phenomena. These parameters have proved to be good predictors of morbidity and mortality in the clinical sphere, despite the need for scientific deepening, with expressive samples and prolonged follow-up. Such studies may be useful in the research and treatment of heart disease. (Vanderlei 2009) Among the nonlinear methods used for HRV analysis, mention must be made of detrended fluctuation analysis, correlation function, Hurst exponent, fractal dimension and Lyapunov exponent (Rajendra 2006). Novel spectral indexes of HRV include Vindex and prevalent low-frequency oscillation of heart rate. Nonlinear HRV indexes also include alpha-1 (a1), beta (b) and the ratio of intermediate-term variability to short-term variability (SD12). They represent the structure of heart rate time series as opposed to the amount of HRV at a particular period of time, as measured with time and frequency domain parameters.

Fantastic terrors their turbulence proclaims

Several studies showed that reduced HRV was associated with the development of many cardiovascular conditions, such as coronary heart disease , hypertension (Konrady 2001), myocardial infarction (Eagle et al 1996) and chronic heart failure (Scalvini 1998), as well as poorer cardiovascular outcomes in those who already have the disease (Lanza 2006). The hypothesis that the reduction of HRV reflects the suppression of vagal modulation and sympathetic dominance resulting in higher mortality and arrhythmia has been used as basis for many studies, that have consistently confirmed it. (Spallone 2011; Vinik 2011)

Decreased ultra low-frequency power (ULF), very low-frequency power, low-frequency power (LF) and/or variance of all N-N intervals have been found to be independently predictive of mortality in patients who experienced an MI (Bigger et al 1992; Routledge 2010). Abnormalities in 24h HRV indexes were found to be predictive of poorer outcomes in the CHF population (Routledge 2010). Although it would appear that short-term and long-term LF spectral components have similar predictive power for CHF patients, there is a greater breadth of evidence in support of the predictive value of 24h HRV assessment.

HRV may be an independent prognostic determinant for individuals with unstable angina. But, although reduced HRV has been found to be associated with a worse prognosis in several cardiac populations, prospective studies have so far found HRV assessment to provide no prognostic significance for individuals who have undergone coronary artery bypass grafting (CABG) surgery. (Milicevic 2004)

Impaired autonomic nervous function of patients with diabetes as assessed by HRV has been observed in otherwise healthy adults (Javorka 2005), as well as in children (Chessa 2002) and adolescents (Boysen 2007). Both short-term and 24h assessments have revealed the prognostic significance of HRV in individuals with type 1 and/or type 2 diabetes. HRV indexes have been associated with carotid intima-media thickness (Gottsater 2006) and progressive renal deterioration (Burger 2002). Decreased HRV in individuals with diabetes has also been found to be predictive of cardiovascular morbidity and mortality (Astrup 2007). Cardiovascular autonomic neuropathy (CAN), in the context of diabetic autonomic neuropathy, occurs when there is an impairment of the autonomic control of the cardiovascular system after ruling out other causes of dysautonomia. (Rolim 2013)

It is well known that CAN is an early and frequent complication of diabetes mellitus (DM), affecting from 7 to 15% of newly diagnosed patients to 90% of those in line for a double transplant. In addition, CAN is one of the most disabling complications of DM in terms of life expectancy and quality. Its detection in a diabetic patient requires sensitive and specific tests in order to establish differential diagnosis and quantify the severity of dysautonomia (Spallone et al 2011). The recent Toronto Consensus concluded that the five most sensitive and specific methods to assess the presence of CAN are: (A) study of HRV using the ratio of the RR intervals of the electrocardiogram (ECG); (B) baroreflex sensitivity (BRS); (C) muscle sympathetic nerve activity (MSNA); (D) measurement of plasma levels of catecholamines (PLC); (E) cardiac sympathetic mapping (CSM).

Overall evidence suggests that HRV assessment has prognostic significance for individuals with MI, CHF, unstable angina and diabetes. Decreased HRV predicts adverse events and poor outcomes. However, there appears to be no prognostic power associated with HRV assessment in the CABG population (Milicevic 2004). HRV has gained importance today as a technique to explore ANS, which has an important role in maintaining homeostasis (Vanderlei 2009). It is used as a predictor of the internal functions of the body, both in normal and pathological conditions. Cost-effectiveness in its application and ease of data acquisition makes HRV an interesting option for interpreting ANS functioning and promising a clinical tool to assess and identify impairments on health.

The bell ringers await us

The behavior of the cardiovascular system and its coupling to the respiratory system is not only of great neurobiological and clinical interest, but it has all the characteristics of a nonlinear dynamical system that is amenable to a quantitative analysis (Garcia 2013). The neuronal control of breathing and heart rate are closely linked, functionally as well as anatomically. Cardiorespiratory coupling (CRC) is perhaps best typified by the occurrence of respiratory sinus arrhythmia (RSA) (Garcia 2013). Many decades ago, researchers suggested that heart rate sinus arrhythmia might be caused by the regulation of cardiac vagal outflow involving the same neuronal processes that generate the respiratory rhythm and reside within the brainstem (Anrep 1936). The close interaction between cardiac and respiratory control synergizes the autonomic functions that are critical for survival. This coupling is not only important for the homeostatic regulation of blood gases, but a tight coupling of cardiorespiratory control is also critical for regulating central nervous functions, such as arousal. Altered cardiovascular function is commonly associated with respiratory diseases and dysautonomias (Cazzola 2012). Respiratory dysfunction can negatively impact cardiovascular health and vice versa. (Garcia 2013)

RSA occurs during both normal breathing (i.e., eupnea) and augmented breathing patterns (i.e., sighs). It is characterized by the increase of HR during inspiration and HR decrease during expiration. RSA is believed to represent a healthy form of HRV and is hypothesized to improve energetic efficiency of gas exchange (Hayano 1996) or alternatively, to assist in reducing cardiac work while maintaining healthy blood gas levels (Ben-Tal 2012). RSA is normally measured and assessed in young healthy individuals as HF HRV. Both sympathetic and parasympathetic activities are responsible for the generation of RSA. (Garcia et al 2013)

The physiological frequency and waveform patterns recorded from patients show very complex dynamics that are reflected in HR fluctuations. While changes in HRV are believed to represent changes in the state of cardiac health associated with changes in sympathetic and parasympathetic balance, the precise physiological basis of HRV still remains an open question. In humans, HR oscillates between 0.003 Hz and 0.5 Hz, and HRV is historically divided into three frequency ranges: very low frequency (0.003-0.004) Hz, low frequency (0.04-0.15) Hz, and high frequency (0.15-0.5) Hz components. Since high frequency range is associated with respiration, the respiration-related instability observed within this frequency band is commonly used to describe RSA (Garcia 2013). Heart rate dynamics is typically more chaotic during daytime recording which is thought to be due to a significant increase of sympathetic drive. Based on this assumption, HR recordings are often performed during nighttime which is thought to provide better insights into the parasympathetic drive. The increase of RSA during the night has been previously reported in healthy individuals (Carroll 2012). Comparison of control individuals to a cohort with familial dysautonomia demonstrates the significant reduction of RSA in individuals with familial dysautonomia-figure 6. A decline in CRC involves several biological mechanisms that appear to converge onto cardio-respiratory functions that predominantly originate in the brainstem.

Between the moment of birth, when HRV is at its highest peak, and the approach of death, when it is at its lowest, we lose around 3% of variability each year (Umetani 1999). This is a sign of the loss of flexibility of our physiology, which can adapt less and less to environmental variations. In fact, it is the sign of aging. If variability diminishes, it is because we do not maintain our physiological brake, i.e. the parasympathetic tonus. Like a muscle hardly used for a long period of time, it will eventually atrophy in time. On the other hand, our accelerator--sympathetic tone--is used all the time (Servan-Schreiber 2003). The fall of HRV is associated with

health problems due to stress and aging: hypertension, cardiac failure, diabetes complications, heart attack, cancer (Servan-Schreiber 2003). When HRV is lost, when the heart does not respond anymore to our emotions and it cannot brake anymore, death approaches. (Dekker 1997; La Rovere 1998)

What a world of enchantment

Humans interact with music, both consciously and unconsciously, at behavioral, emotional, and physiological levels. Various studies insist that it acts on heart activity, by the ability to affect ANS activity; however, few studies systematically explored the therapeutic effects of music on ANS dysfunction. Given that ANS is both associated with physiological health and responsive to music, it may serve as one path by which music exerts its therapeutic effect. (Ellis 2010)

A number of studies have reported that listening to sedative music (i.e., slow tempo, legato phrasing, and minimal dynamic contrasts) can lead to decreased heart rate, respiration rate, and blood pressure. Importantly however, these effects are inconsistent (Ellis et al 2010). Also, randomized controlled trials have reported that music possesses anxiolytic and analgesic properties, and is associated with decreased HR, respiration rate and blood pressure in perioperative patients.

Data in literature reported spontaneous entrainment of blood pressure and respiration rate to musical tempo. Entrainment is the process by which two oscillating systems assume the same period (or period ratio) when they interact (Ellis 2010). In experimental paradigms, entrainment usually refers to the synchronization of endogenous rhythms in the subject with an exogenous rhythm in the environment. Endogenous rhythms exist at many orders of magnitude in a number physiological processes, such as reproduction and menstruation (-30 days), sleep-wake (-24 hours), rapid-eye-movement sleep (~3 hours), blood pressure (~0.1-0.15 Hz), breathing (-0.15-0.4 Hz), cardiac pulse (-1-2 Hz), and electroencephalographic activity (-1-100 Hz). (Ellis et al 2010)

Singing can reduce tension, increase energy, and improve mood in healthy subjects. It is well known that choir singing promotes wellbeing. One reason for this may be that singing demands a slower than normal respiration, which may in turn affect heart activity. A study demonstrated that the impact of singing on physiological activity seems dependent on how the task is perceived by the subjects. Researchers found that professional singers showed greater HRV after a singing lesson, whereas amateur singers showed HRV after the lesson (Grape et al 2003). Another study (Valentine et al 2001), reported a slight (2.5 bpm) increase in heart rate after solo singing, but a comparable decrease after choral singing. Thus, how a task is perceived by the subjects should be considered when examining how that task affects physiological activity. (Ellis et al 2010)

On night's sweet wind softly they sing their spell

Many studies have been experimental rather than interventional, reporting significant changes in HRV as a function of musical mood (Etzel 2006), genre (Bernardi 2006), familiarity (Iwanaga 2005) or tempo. We know of a few reports on musical interventions that includes HRV as an index of autonomic function: in pediatric oncology patients (Kemper 2008), myocardial infarction patients (White 1999) and geriatric patients. Researchers compared changes in mean HR and HRV while subjects listened to 2.5-minute excerpts of music at three different tempos (60, 90, 120 bpm). HRV decreased as tempo increased, indicating that the "challenge" of arousing music prompted a withdrawal of PNS activity. No significant change was observed in mean HR, however, despite significant correlations between mean HR and measures of HRV at baseline (all r(28) > [absolute value of .69], all p < 0.001). (Ellis et al 2010)

In a spectral analysis of HR variation waveforms as a result of music stimulation, examination of the respiratory variation component in the vicinity of 0.25 Hz (respiratory sinus arrhythmia) and the arterial blood pressure variation component in the vicinity of 0.1 Hz (Mayer wave sinus arrhythmia) showed that the RSA component tended to increase when subjects were concentrating on music than when they were resting, suggesting that music affects the parasympathetic nervous system (Hoshishiba 1995). It was noticed that when favorite music decreases anxiety and induces relaxation, heart rate, muscle activity, vascular constriction and peripheral skin temperature are stimulated rather than depressed (David & Thaut 1989). The results of another study showed that the higher the sympathetic nerve activity was before exercise, the higher LF/HF was after exercise, regardless of whether the subject listened to music, as indicated by significant correlations between LF/HF before and after exercise. It appeared that after the exercise in which sympathetic nerve activity is dominant, favorite music acts in synchrony with the activated physical response to further promote the response and increase sympathetic nerve activity. (Urakawa et al 2005)

An interesting recent study also showed that singing increases HRV (Vickhoff et al 2013), that the type of singing has a different effect on HRV. While humming does not produce a significant increase in HRV as measured by RMSSD (the root mean square of the successive differences Degiorgio 2010), one could conclude that humming does lead to a significantly more regular HRV (0.05-0.1 Hz), as measured by the frequency score (p-value < 0.01). That is to say, HR acceleration and deceleration is quite regular during humming, although the rate of the fluctuation is highly individual. This is statistically verified by the non-significant coherence between the humming segment HR curves of subject pairs (Vickhoff 2013). HRV as measured by RMSSD is significantly increased during hymn singing compared to both baseline and humming (p-value < 0.05), and also exhibits significantly lower LF/HF ratio compared with the other conditions (p-value < 0.05). However, frequency analysis indicates that HR fluctuations are not as regular as during humming (p-value < 0.01). Finally, mantra produces a significantly higher RMSSD compared to all other conditions (p-value < 0.01), as well as significantly more regular HRV (frequency score) compared with baseline and humming (but not hymn singing) (p-value < 0.01). The coherence analysis clearly shows that mantra singing induces a strong, shared HRV frequency component at 0.1 Hz, significantly higher compared to all other conditions (p-value < 0.01). Taken together, such results indicate a strong connection between song structure and HR patterns. (Vickhoff et al 2013)

It is about the fundamental question of why music is a universal phenomenon. Unlike in most other universal human behaviors, there is no self-evident Darwinian explanation. A well-known American cognitive musicologist, though, suggested that music stimulates the production of the neuropeptide oxytocin and thus strengthens bonding (Huron 2006). The question of music and oxytocin is rather complicated. It is, due to the blood/brain barrier, difficult to get direct indications of oxytocin in the brain. Venous oxytocin can be obtained, however. On the other hand, the oxytocin level increase was noticed after singing lessons (Grape 2003). If collective singing creates joint perspectives, it would indeed be bonding in the deepest sense. (Vickhoff et al 2013)

In conclusion the vagal effect of breathing is, as pointed out, an ANS reaction. It is hardwired and thus universal. It is thus expected that various cultures use this technique wherever people gather to achieve relaxed communicative states. Interestingly so, coordinated respiratory activity, irrespective whether caused by yoga breathing, mantra chanting, praying or singing is ritually performed in most religions. This is a common factor, more so than the semantic content of beliefs.


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Serban Ardeleanu Gr. T. Popa University of Medicine

Daniela Ardeleanu Mihai Eminescu High School, Iasi

Correspondence concerning this article may be addressed to or

Fig. 4. Spectral analysis of frequencies (Fast Fourier
Transform) of a normal young adult (A) and a
normal newborn (B). The high frequency (HF)
component is proportionally smaller in the
newborn (arrows) as well as the total power (from
Vanderlei et al 2009)

       Power     Power   Power
     [ms.sup.2]   (%)    (n.u.)

VLF     550      25.7
LF      792      37.0    49.7
HF      800      37.3    50.3

LF / HF = 0.990


       Power     Power   Power
     [ms.sup.2]   (%)    (n.u.)

VLF     186      42.1
LF      196      44.4    76.6
HF       60      13.6    23.4

LF / HF = 3.270

Note: Table made from bar graph.
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Title Annotation:The bells, bells, bells
Author:Ardeleanu, Serban; Ardeleanu, Daniela
Publication:Romanian Journal of Artistic Creativity
Date:Dec 22, 2014
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