Aim of the experiment was to compare heart rate and HRV of healthy subjects with high (Highs) and low (Lows) susceptibility to hypnosis during long-lasting relaxation. HRV indexes extracted in the frequency and time domain as well as related to the complexity of the tachogram (entropy) were analyzed through repeated measures ANOVA. The results indicated a parasympathetic prevalence in Highs all over the session and a sympathetic modulation across the session in both groups reflected in the sd2 dimension of the Poincar? plot and in entropy. The possible role of the Very Low Frequency spectral component in these changes, supported by the different correlations between entropy and frequency/time related indexes of HRV, is suggeste

Balocchi, Rita

Carli, Giovanni

Morizzo, Carmela

Palombo, Carlo

Paoletti, Giancarlo

Santarcangelo, Enrica

Varanini, Maurizio

PUblication MAnagement

5th Conference of the European Study Group on Cardiovascular Oscillations 
P1C-36 
  
Abstract— Aim of the experiment was to compare heart rate and HRV of healthy subjects with high (Highs) 
and low (Lows) susceptibility to hypnosis during long-lasting relaxation. HRV indexes extracted in the 
frequency and time domain as well as related to the complexity of the tachogram (entropy) were analyzed 
through repeated measures ANOVA. The results indicated a parasympathetic prevalence in Highs all over 
the session and a sympathetic modulation across the session in both groups reflected in the sd2 dimension of 
the Poincaré plot and in entropy. The possible role of the Very Low Frequency spectral component in these 
changes, supported by the different correlations between entropy and frequency/time related indexes of 
HRV, is suggested. 
 
I. INTRODUCTION 
The susceptibility to hypnosis, or hypnotizability, is a cognitive trait responsible for the possibility 
to accept suggestions and modulate perception and behaviour accordingly (2). It can be measured 
through scales and has its anatomo-functional basis in the supervisory attentional system, located in 
frontal and cingulate cerebral areas.  
In non hypnotic conditions and without specific suggestions, individuals with high (Highs) and low 
susceptibility to hypnosis (Lows) differ in the performance of many tasks in the cognitive, somatic 
and autonomic domain. In particular, studies of endothelial function allow hypothesizing a natural 
protection of Highs against the vascular effects of cognitive stress and acute pain (3,4) and studies 
of heart rate and heart rate variability in subjects receiving various stimulations (1,5) suggest that 
Highs might be less vulnerable than Lows to heart disease.  
At variance with stimulation conditions, the differences between Highs and Lows in basal (no task) 
conditions are less clear-cut. In particular, some authors report a higher heart rate and a lower vagal 
tone in low (Lows) with respect to highly susceptible subjects (Highs), while others have found no 
significant difference between Highs and Lows in heart rate and in heart rate variability (see ref 6). 
However, in spite of the lack of difference between not hypnotized Highs and Lows in heart rate and 
HRV along a long-lasting relaxation period, previous finding from our laboratory showed different 
EEG activities suggesting an increased and decreased integrative activity in Highs and Lows, 
respectively (6). This suggested that relaxation can be considered a cognitive task and that the 
cardiac output observed might be the result of complex control mechanisms not reflected in the 
HRV indexes considered, but related to the Very Low Frequency component of the RR power 
spectrum and/or to components induced by non stationarities of heart activity. Since decreased 
entropy is expected when the sympathetic activity increases (11), aim of the present study was 1) to 
identify possible hypnotizability related changes in the HRV complexity occurring during long-
lasting relaxation and 2) to compare the sensitivity of complexity indexes with that exhibited by the 
classical time and frequency domain HRV indexes.  
 
II. METHODS 
After signing an informed consent, 8 Highs and 10 Lows (healthy males, age 18-25), selected 
according to the Stanford Hypnotic Susceptibility Scale, form C joined the study. They were asked 
to relax as much as possible according to their usual strategies (simple relaxation) for 15 minutes, 
with eyes closed but without hypnotic induction. The RR time series, obtained from the recorded 
ECG sampled at 1000 Hz, were corrected from artefacts using a linear predictive filter with a 
threshold on the prediction error. A posteriori the session was divided into 3 consecutive epochs 
(E1, E2, E3), each of them including 300 beats.  
The following parameters, mean RR (RRm), RR standard deviation (SD), root mean square of 
successive RR differences (RMSSD) (7), normalized powers in low frequency (LFn) and high 
frequency (HFn) bands, LF/HF (7), sd2 and sd1 dimensions of the Poincaré plot and their ratio (CSI, 
cardiac sympathetic index) (8), approximate entropy (ApEn) (9), sample entropy (SmE) (10) and 
corrected conditional entropy (CCE) (11) were computed for each epoch. The normalized power of 
Hypnotizability-related complexity of heart rate variability 
during long lasting relaxation. 
E. L.Santarcangelo, R. Balocchi, G. Paoletti, C. Palombo, C. Morizzo, G. Carli, M. 
Varanini 
5th Conference of the European Study Group on Cardiovascular Oscillations 
P1C-37 
the Very Low Frequency spectral component (VLFn) was also computed all over the session 
(15min).  
Spectral analysis was performed according to Welch method, using a frequency resolution of 
0.0167Hz; window length, overlapping and number of intervals were automatically adjusted 
according to the effective time duration of the 300 beats epoch. Zero padding to 512 length was 
performed.  
The sd1 and sd2 dimensions of the Poincaré Plot as well as the ratio sd2/sd1 (cardiac sympathetic 
Index, CSI) were also computed (2); sd1 contains only the short-term RR variability, while sd2 
includes the HRV corresponding to both the low (LF) and Very Low (VLF) Frequency spectral 
components. 
 Entropy-based complexity indices were estimated using the following parameter settings: ApEn, 
pattern length m=2, tolerance r= 0.2 times standard deviation; SmE, pattern length m=3, tolerance as 
for ApEn; CCE, pattern length m=3, number of quantization levels=6. Statistical analysis of RRm, 
SD, RMSSD, LFn, HFn, LF/HF, sd2, sd1, CSI, ApEn, SmE and CCE was performed through 
repeated measures ANOVA following a 2 Groups (Highs, Lows) x 3 epochs (E1, E2, E3) 
experimental design. Contrast analysis was used when appropriate. The VLFn values of Highs and 
Lows all over the session were compared through univariate ANOVA. Spearman correlation 
coefficients were computed between entropy (SmE,CCE), sd2 and LFn in order to detect a possible 
role of VLFn in the time related changes of entropy. Level of significance was set at p<0.05. 
  
III. RESULTS 
ANOVA revealed a significantly increase in RR during E2 (F(1,16)=8.219, p<0.011) and E3 
(F(1,16)=14.408, p<0.002) with respect to E1, but no Group difference was observed. RR and HRV 
indexes values are shown in Table 1. 
Frequency domain indexes - No significant Epoch effects were observed for LFn, HFn and LF/HF. 
A significant Group effect was found for HFn which was significantly greater in Highs ( (F(1,16)= 
9.426, p<0.007) all over the session. Normalized values of the VLF components (VLFn, computed 
on the entire session) were significantly higher (Group effect, (F(1,16)=5.706, p<0.030) in Lows 
(242 + 0.021) than in Highs (168 + 0.023).  
Time domani indexes - SD and RMSSD did not change significantly across epochs and did not 
differentiate Groups; in the Poincaré Plot, sd2 decreased significantly during E2 with respect to E1 
(F(1,16 )= 4.764, p < 0.044), while sd1 and CSI did not change significantly across epochs. A 
significant Group effect was observed for CSI, which was lower in Highs all over the session 
(F(1,16 ) =7.442, p<0.015).  
Entropy - No Group effect was found for entropy. ANOVA revealed a significant increase in SmE 
(F(1,16)= 10.278, p<0.006) and CCE (F(1,16)=5.683, p< 0.030) during E2 with respect to E1, but 
no significant change in ApEn (E2 vs E1, p=0.057). Thus, from a methodological point of view, our 
findings confirm that SmE and CCE measures are more suitable to estimate complexity of short term 
HRV with respect to ApEn which does not include the correction of the bias due to short time series 
(11). 
Entropy (SmE and CCE) showed a time course similar to sd2 and different from LFn. In line with 
this observation, the linear correlation coefficients computed for each epoch showed 1) the absence 
of significant correlations between LFn and sd2 as well as between both SmE and CCE and LFn; 2) 
the occurrence of a significant negative linear correlation between SmE and sd2 (R = -0.542, 
p<0.021) and of a quasi significant correlation between sd2 and CCE (R=-0.461, p=0.054) during 
E3. 
 
TABLE I.  
 E1  E2 E3  
variable group mean SE mean SE mean SE 
RR H 838,87 45,35 863,75 46,91 861,11 45,48
  L 811,21 40,56 820,66 41,96 821,56 40,68
             
SD H 60,99 5,49 52,95 7,27 62,13 7,62 
  L 57,16 4,91 52,50 6,50 62,54 6,81 
             
5th Conference of the European Study Group on Cardiovascular Oscillations 
P1C-38 
RMSSD H 45,31 4,35 44,68 6,13 54,86 10,23
  L 34,07 3,89 32,91 5,48 38,87 9,15 
             
LFn H 0,46 0,04 0,44 0,04 0,46 0,04 
  L 0,53 0,03 0,56 0,04 0,53 0,04 
             
HFn* H 0,37 0,03 0,41 0,05 0,37 0,03 
  L 0,24 0,03 0,23 0,05 0,22 0,03 
             
LF/HF H 1,49 0,60 1,39 0,75 1,45 0,72 
  L 2,79 0,54 3,52 0,67 3,21 0,65 
             
sd1 H 32,09 3,08 31,65 4,34 38,86 7,25 
  L 24,13 2,75 23,31 3,88 27,53 6,48 
              
sd2 H 79,71 7,53 67,53 9,65 78,48 13,97
  L 76,95 6,73 70,17 8,63 80,91 12,50
             
CSI* H 2,48 0,29 2,21 0,27 2,35 0,29 
  L 3,21 0,26 3,05 0,24 3,04 0,26 
             
SmE H 1,37 0,12 1,61 0,13 1,58 0,12 
  L 1,26 0,11 1,39 0,12 1,34 0,11 
             
CCE H 0,98 0,08 1,15 0,07 0,98 0,07 
  L 0,84 0,07 0,92 0,06 0,97 0,07 
             
  L 0,99 0,04 1,00 0,03 0,98 0,04 
* significant difference between Groups 
 
 
IV. DISCUSSION 
The results failed to identify time-related differences between the Highs’ and Lows’ heart rate and 
HRV during long-lasting relaxation, but indicated a parasympathetic prevalence in the former all 
over the session. 
At variance with previous findings (6) showing an increase in RR during long-lasting relaxation 
only in Lows, RR increased in both groups, which might be due to gender, since only males were 
included in the present experiment. The changes occurring in spectral indexes across the consecutive 
epochs do not account for the RR increases observed, while sd2, extracted from the Poincaré Plot, 
seems to be a more sensitive index of sympathetic-parasympathetic balance, likely because it 
includes the contribution of the very Low Frequency spectral component. 
Since entropy increases across epochs, paralleling the RR increases and sd2 decreases, it might be 
hypothesized that this complexity index reflects changes including Low and Very Low Frequency 
spectral components, as suggested also by the linear correlations observed between entropy and sd2, 
but not between entropy and LFn. This is not in contrast with previous findings suggesting that 
when LFn decreases entropy increases (11), but further supports the role of VLF in the autonomic 
space. Unfortunately VLF cannot be reliably evaluated along short time intervals. However, its 
prevalence in Lows all over the session might explain the significant Group difference observed for 
CSI and not for LF/HF between the two groups.  
Entropy-based measures of HRV did not differentiate between Highs and Low, while both HFn and 
CSI revealed a parasympathetic prevalence in Highs all over the long-lasting relaxation. This 
suggests that the Group differences observed are, mainly, due to parasympathetic components, while 
the time related changes occurring in both Highs and Lows are the results of a pre-eminent 
5th Conference of the European Study Group on Cardiovascular Oscillations 
P1C-39 
sympathetic modulation. Finally, the analysis of HRV in the frequency domain seems to be more 
sensitive to group differences, while the indexes extracted in the time domain are more responsive to 
the time-related changes occurring in heart rate control in both groups during long-lasting 
relaxation.  
ACKNOWLEDGMENT 
Research granted by the Italian Spatial Agency (ASI-DCMC project). 
REFERENCES 
[1] R. Balocchi, M.Varanini, D.Menicucci, E.L. Santarcangelo, S. Migliorini, G. Fontani, G. Carli (2005) Heart rate 
variability in subjects with different hypnotic susceptibility receiving nociceptive stimulation and suggestions of analgesia. 
IEEE Eng Med Biol., 6996-99. 
[2] J.P. Green, A.F. Barabasz, D. Barrett, G.H. Montgomery (2005). Forging ahead: the 2003 APA Division 30 definition of 
hypnosis. Int. J. Clin. Exp. Hypn., 53, 259-64. 
[3] Z. Jambrik, E.L. Santarcangelo, B. Ghelarducci, E. Picano, L. Sebastiani (2004) Does hypnotisability modulate the stress-
related endothelial dysfunction. Brain Res. Bull., 63, 213-21. 
[4] Z. Jambrik, G. Carli, T.Rudish, A. Varga, T. Forster, E.L. Santarcangelo (2005). Modulation of pain-induced endothelial 
dysfunction by hypnotisability. Pain, 116, 181-86. 
[5] E.L. Santarcangelo, R. Balocchi, E. Scattina, D. Manzoni, L. Bruschini, B. Ghelarducci, M. Varanini (2008). 
Hypnotizability-dependent modulation of the changes in heart rate control induced by upright stance . Brain Res. Bull., in 
press. 
[6] L. Sebastiani, A. Simoni, A. Gemignani, B. Ghelarducci, E.L. Santarcangelo (2005). Relaxation as a cognitive task. Arch. 
Ital. Biol., 143, 1-12. 
[7] Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 
(1996). Standard of measurements, physiological interpretation and clinical use. Circulation, 93, 1043-65. 
[8] R. Balocchi, F. Cantini, M. Varanini, G. Raimondi, J.M. Legramante, A. Macerata (2006). Revisiting the potentials of 
time-domain indexes in the short-term HRV analysis. Biomed. Tech., 51, 190-93. 
[9] S.M. Pincus (1995). Approximated entropy (ApEnn) as a complexity measure. Chaos, 5, 110–17. 
[10] J.S. Richman, J.R. Moorman (2000). Physiological time-series analysis using approximate entropy and sample entropy. 
Am J Physiol, 278, H2039 - H2049. 
[11] A. Porta, T. Gnecchi-Ruscone, E. Tobaldini, S. Guzzetti, R. Furlan, N. Montano (2007). Progressive decrease of heart 
period variability entropy-based complexity during graded head-up tilt. J Appl Physiol, 103, 1143-49.  
 


Hypnotizability-related complexity of heart rate variability during long lasting relaxation

http://puma.isti.cnr.it/rmydownload.php?filename=cnr.ifc/cnr.ifc/2008-A2-004/2008-A2-004.pdf

Hypnotizability-related complexity of heart rate variability during long lasting relaxation

Abstract

Similar works

Full text

Available Versions

PUblication MAnagement