IF: 0.644

Comparative Study of HRV Indexes Between Severe and Non-Severe Obstructive Sleep Apnea Patients


Mehrnaz Asadi Gharabaghi 1 , * , Masoud Ehtesham 1 , Reza Molazadeh 2 , Shahram Firoozbakhsh 1 , Enayat Safavi 1

1 Department of Pulmonary Diseases, Advanced Thoracic Research Center, Tehran University of Medical Sciences, Tehran, IR Iran

2 Department of Cardiology, Tehran University of Medical Sciences, Tehran, IR Iran

How to Cite: Asadi Gharabaghi M, Ehtesham M, Molazadeh R, Firoozbakhsh S, Safavi E. Comparative Study of HRV Indexes Between Severe and Non-Severe Obstructive Sleep Apnea Patients, Iran Red Crescent Med J. 2016 ; 18(2):e29356. doi: 10.5812/ircmj.29356.


Iranian Red Crescent Medical Journal: 18 (2); e29356
Published Online: February 20, 2016
Article Type: Research Article
Received: April 23, 2014
Revised: June 2, 2015
Accepted: December 23, 2015




Background: The temporary cessation of breathing, or obstructive sleep apnea, is a known risk factor for cardiovascular disease. The autonomic cardiac measurement via the heart rate variability (HRV) is a known identification method for heart conditions during sleep, which identifies their relationship with the apnea-hypopnea index (AHI), and can help to detect disease severity. Additionally, it helps to estimate the risk of heart problems and begin treatment of the disease (according to the HRV), and in mild cases, the AHI can help reduce the risk of cardiovascular problems in patients.

Objectives: This study was conducted to compare the HRV in patients with severe and non-severe obstructive sleep apnea.

Patients and Methods: This cross-sectional study contained 36 patients suffering from obstructive sleep apnea who were referred to the sleep clinic of the Imam Khomeini hospital in Tehran, Iran, in 2015. Apnea syndrome in these patients was confirmed by polysomnography, and the participants were divided into two groups, severe (AHI ≥ 30) and non-severe (AHI = 5 - 29). The age, BMI, resting heart rate, and obtained HRV indexes via a sleep test (polysomnography) were recorded for each patient. The statistical analysis was done by SPSS version 19.

Results: The statistical analysis did not show any significant differences in the age and BMI between the two groups. Moreover, the results showed that only the SDNNi index (P value = 0.026) and ODI index (P value = 0.001) were significantly different between the two groups. The AHI had the greatest correlation with the HRV-triangular index (P = 0.022, rp = 0.371), between the HRV indexes, and with the oxygen desaturation index (ODI) (P = 0.001, rp = 0.63), among all of the parameters.

Conclusions: According to the results of this study, the majority of the HRV indexes did not significantly differ between the two groups, with only the ODI index differing significantly. Therefore, it seems that a clinical judgment cannot be made based on the index data in all societies.


Obstructive Sleep Apnea Syndrome (OSAS) Heart Rate Variability (HRV) Apnea-Hypopnea Index (AHI) Oxygen Desaturation Index (ODI)

Copyright © 2016, Iranian Red Crescent Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Sleep apnea is a sleep disorder characterized by pauses in breathing, or instances of shallow or infrequent breathing during sleep. Each pause in breathing, called an apnea, can last for several seconds to several minutes, and may occur, by definition, at least 5 times in an hour (1). Sleep apnea is classified as a dyssomnia, meaning abnormal behavior or psychological events that occur during sleep (2). There are three forms of sleep apnea: central (CSA), obstructive (OSA), and complex or mixed sleep apnea (i.e. a combination of central and obstructive), constituting 0.4%, 84%, and 15% of the cases, respectively (3).

Obstructive sleep apnea syndrome (OSAS) during sleep is a common disorder that afflicts about 7% of the population, and this syndrome is more common in men than women (4). Symptoms of this syndrome are poor sleep, sometimes severe snoring, and daytime sleepiness and fatigue. The complications of OSAS include depression, lack of glycemic control, heart attacks, arrhythmias, and cardiovascular complications such as high blood pressure (5-9). In addition, its association with vascular pathology, especially atherosclerosis, has been recognized. An alternating and frequent decline in the blood oxygen saturation, increased respiratory effort to open the sympathetic tone collapsed airway, and the renin-angiotensin-aldosterone system (RAAS) are the most probable pathophysiological mechanisms of this syndrome in the progress of cardiovascular disease. Accordingly, the evaluation of cardiovascular function in these patients is a necessary action.

In 1981, Akselrod et al. proved the relationship between the heart rate variability (HRV) and the autonomic nervous system (10). The HRV is the physiological phenomenon of variation in the time interval between heartbeats, and is measured by the variation in the beat-to-beat interval (11). The index analysis of the HRV is a non-invasive method known in the evaluation of the cardiac autonomic nervous system; the relationship between the disturbances of the HRV indexes has been demonstrated with increased mortality in animals, and this method has become a predictive tool for angina and myocardial infarction (MI) (12, 13). Reduced HRV has been shown to be a predictor of mortality after myocardial infarction (MI) (14), although other studies have shown that the information in the HRV relevant to acute myocardial infarction survival is fully contained in the mean heart rate (15). A range of other outcomes/conditions may also be associated with a modified (usually lower) HRV, including congestive heart failure, diabetic neuropathy, depression, post-cardiac transplant, susceptibility to SIDS, and poor survival in premature babies.

The HRV index changes in OSAS have been evaluated in some studies (16-18), even suggesting that this method is a proper technique for screening (19). Therefore, the cardiovascular status could be evaluated by the determination of these indexes with relation to the severity of this syndrome, which is classified based on the apnea-hypopnea index (AHI). In addition, one can determine which of the indexes have better relationships with the disease severity, and be more helpful in clinical assessment and treatment. Moreover, these indexes can be used to evaluate the starting time and follow up treatment, such as using continuous positive airway pressure (CPAP) (20).

2. Objectives

Determining the changes in patients’ HRV indexes based on the severity of their disease, and finding the best differentiated index or indexes will be helpful for clinical judgment. Therefore, this study was conducted to compare the HRV indexes for severe and non-severe obstructive sleep apnea in patients referred to the sleep clinic of the Imam Khomeini hospital in 2015.

3. Patients and Methods

In this cross-sectional study, 39 patients suffering from OSAS referred to the sleep clinic of the Imam Khomeini hospital in Tehran, Iran, were recruited. The sampling was via census, and the total volume of the sample was calculated according to Equation 1. Those patients with cardiac arrhythmias, hypertension, a history of ischemic heart disease (IHD) (known coronary disease), and thyroid function disorders, and those patients treated with beta-blocker drugs, calcium blockers, anti-arrhythmics, autonomic nervous system drugs (either inhaled or oral, such as bronchodilators), and addicted to narcotics or drugs affecting autonomic dysfunction, were excluded from this study.

Equation 1.

This formulas was used to calculate the volume of the sample; n = number of samples, α = 0.05, β = 0.2, µ1 = 17.3, µ2 = 21.6, σ1 = 4.7, σ2 = 7.8, Z1-α/2 = 1.961150776, and Z1-β = 0.841623031.

The patients with symptoms of OSAS were examined by polysomnography using an Embla N7000 machine and Medcare Embla software (Reykjavik, Iceland), and were diagnosed by analyzing the electroencephalogram (EEG) and respiratory movements. The participants were divided into two groups, severe (AHI ≥ 30) and non-severe (AHI = 5 - 29), according to the number of apnea episodes (apnea for at least 10 seconds) and hypopnea episodes (decrease in respiratory flow for at least 10 seconds) per hour of sleeping.

The age, body mass index (BMI), resting heart rate, and obtained HRV indexes via a sleep test (polysomnography) were recorded for each patient. The code sheet and master sheet data were entered into SPSS software version 19 for the statistical analysis. For the frequency of the qualitative variables and for the quantitative variables, the mean, range, and standard deviation were calculated. The t-test and chi-square test were used for the statistical analysis, and a P-value < 0.05 was considered to be significant.

With regard to ethical considerations, the principles of the Helsinki Declaration were considered in this study.

4. Results

In this study, the participants consisted of 26 males and 13 females, and were divided into two groups: severe (AHI ≥ 30) and non-severe (AHI = 5 - 29). The severe group contained 19 participants, and the mean age and BMI were 52.29 ± 13.64 and 34.23 ± 9.22, respectively. The non-severe group contained 20 patients, and the mean age and BMI were 53.43 ± 9.59 and 31.08 ± 7.29, respectively. The statistical analysis showed no significant differences between the two groups for these parameters (P value for age = 0.51, P value for BMI = 0.24).

The time domain and frequency domain parameters were evaluated in this study. The results showed that only the SDNNi index (P value = 0.026) and blood oxygen desaturation index (ODI) (P value = 0.001) were significantly different between the two groups, while the differences between the other indexes were not significant (Table 1).

Table 1. Time Domain and Frequency Domain of the HRV Indexes and ODI in Patients With Severe and Non-Severe Obstructive Sleep Apneaa
VariablesSevereNon-SevereP Value
SDNN117.29 ±63.9587.41 ± 55.090.184
SDNNi101.59 ±65.2661.32 ± 36.370.026
SDANN86.06 ± 70.5888.23 ± 88.230.835
NN506161.35 ± 7412.163481.14 ± 3943.580.343
HF5146.95 ± 3932.24151 ± 2032.80.395
LF20075.94 ± 25549.6814581.5 ± 9046.80.361
VLF28228.12 ± 31437.517755.82 ± 9507.290.118
Total53953.24 ± 55921.538366.91 ± 19480.470.230
LF/HF4.83 ± 4.464.75 ± 6.130.448
HRV17.41 ± 7.2514.59 ± 7.10.401
Desat (ODI)60.29 ± 26.1919.45 ± 19.910.001

aValues are expressed as mean ± SD.

The analyses of the data for the relationship between the AHI and HRV time dependent indexes showed that the SDNN (P value = 0.038, rp = 0.337), SDDNi (P value = 0.016, rp = 0.338), and HRV-triangular (P value = 0.022, rp = 0.371) indexes had direct and significant correlations with the AHI. However, the SDANN (P value = 0.745, rp = 0.054) and NN50 (P value = 0.182, rp = 0.221) had direct correlations with the AHI, but they were not significant (Table 2).

Table 2. Relationship Between the Time Domain HRV Indexes and AHI in OSAS Patients
P Valuerp
NN50 count0.1820.221
HRV triangular index0.0220.142

The relationships between the AHI and frequency domain HRV indexes are shown in Table 3; however, the statistical analysis showed that there was no significant relationship between them. Our results illustrated that the LF (P value = 0.445, rp = 0.125), VLF (P value = 0.332, rp = 0.162), and total power (P value = 0.395, rp = 0.142) indexes had direct relationships with the AHI, while the HF (P value = 0.360, rp = -0.153) had a reverse relationship with the AHI.

Table 3. Relationships Between the Frequency Domain HRV Indexes and the AHI in OSAS Patients
P Valuerp
Power HF0.360- 0.153
Power LF0.4550.125
Power VLF0.3320.162
Power total0.3950.142

Additionally, the relationships between the AHI and the blood ODI, age, and BMI were statistically analyzed, and are shown in Table 4.

Table 4. The Relationships Between the ODI, age, and BMI and the AHI in OSAS Patients
P Valuerp
Age0.903- 021

5. Discussion

The aim of this study was to compare the HRV indexes, such as the SDNN, SDNNi, NN50 count, SDANN, and HRV-triangular between severe and non-severe obstructive sleep apnea patients referred to the sleep test section of the Imam Khomeini hospital in Tehran, Iran, in 2015. In addition, we attempted to determine these parameters in relation to the AHI.

In a number of studies, the changes in the indexes of the HRV in OSAS have been examined, and relationships between the severity of OSAS and the HRV parameters have been found. For example, Roche et al. analyzed the time domain parameters in 39 patients divided into two groups according to their AHIs. Based on their results, these indexes were generally considered to be screening tools, and they found a significant correlation between the disease severity and time indexes (19). In addition, Narkiewicz et al. compared three groups of patients with normal controls, and found that the parameters of the LF/HF ratio, HF power, and LF power in the moderate and severe groups were higher when compared to the control group, and that the LF/HF ratio in these groups was higher in comparison to the mild group (21). Gula et al. stated that the LF/HF ratio in patients with moderate OSAS was even higher than the severe and the control groups (22).

In other research, Aydin et al. studied three groups of participants: control, severe, and non-severe. They reported that the frequency domain of the HRV parameters was higher in the patient groups; however, the LF index and LF/HF ratio were higher in the severe group (23). Similar to our results, Yang et al. reported that the blood ODI showed a significant difference between the severe and non-severe patients (24). Park et al. in a study conducted on 59 patients with untreated OSAS in Korea, reported that all of the indexes in the severe group were higher when compared to the non-severe group, and the frequency domain indexes, especially the LF/HF ratio, were better associated with the severity of the AHI (25).

In this study, the results obtained did not show any significant differences between the two groups for the age and BMI; therefore, it can be concluded that these two factors do not have any effect on the internal autonomy system and analyzed data.

Our results with regard to the HRV indexes were contradictory to some other studies. For example, Park et al. demonstrated that most of the parameters were significantly higher in the severe group, and considered these parameters as a criterion for decision-making (25). In this study, none of the HRV indexes, except the SDNNi, were significantly different between the two groups of patients with OSAS; however, the ODI, as in most studies, was significantly different (Table 1). Similar to our results, Yang et al. showed a lack of differences between the severe and non-severe groups as well. It can be concluded that the HRV indexes are not reliable for clinical decisions and treatment procedures, when used instead of the AHI (24).

The similarity in these parameters could have occurred for various reasons, such as time domain index differences according to sleep phases, ethnic differences in autonomic disorders, latent diseases (e.g. impaired fasting glucose), HRV assessment method (overnight instead of 24-hour holter monitoring), and the most important one, impaired autonomic status in patients with low AHIs. Based on Tables 2 and 3, with the exception of the HF power index, most of the HRV parameters had direct relationships with the AHI index, and these relationships were significant for the SDNN, SDNNi, and HRV-triangular. Therefore, according to the results of this study and other studies, it seems that these indexes can be proposed as indicators of disease severity. In addition, this test can be suggested to determine the effects of OSAS on the cardiovascular system, despite other normal diagnostic procedures, particularly in patients with severe AHI.

In spite of the lack of significant differences between the HRV indexes in the severe and mild group in a study by Yang et al. the ODI had a significant and direct relationship between those two groups. Our results illustrated a significant difference for the ODI as well (24).

Based on the results of this study, it can be concluded that the SDNN, SDNNi, and HRV-triangular can be proposed as indicators of the severity of obstructive sleep apnea in our samples. However, when comparing our results with other studies, it can be concluded that the HRV indexes are not reliable for clinical decisions and treatment procedures, when used instead of the AHI.



  • 1.

    Sleep apnea: what is sleep apnea. Health Information for the Public NHLBI. 2010;

  • 2.

    Green S. Biological Rhythms, Sleep and Hypnosis. 2011;

  • 3.

    Morgenthaler TI, Kagramanov V, Hanak V, Decker PA. Complex sleep apnea syndrome: is it a unique clinical syndrome? Sleep. 2006; 29(9) : 1203 -9 [PubMed]

  • 4.

    Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993; 328(17) : 1230 -5 [DOI][PubMed]

  • 5.

    Powell NB, Schechtman KB, Riley RW, Li K, Guilleminault C. Sleepy driving: accidents and injury. Otolaryngol Head Neck Surg. 2002; 126(3) : 217 -27 [PubMed]

  • 6.

    Teran-Santos J, Jimenez-Gomez A, Cordero-Guevara J. The association between sleep apnea and the risk of traffic accidents. Cooperative Group Burgos-Santander. N Engl J Med. 1999; 340(11) : 847 -51 [DOI][PubMed]

  • 7.

    Pashayan AG, Passannante AN, Rock P. Pathophysiology of obstructive sleep apnea. Anesthesiol Clin North America. 2005; 23(3) : 431 -43 [DOI][PubMed]

  • 8.

    Peker Y, Hedner J, Norum J, Kraiczi H, Carlson J. Increased incidence of cardiovascular disease in middle-aged men with obstructive sleep apnea: a 7-year follow-up. Am J Respir Crit Care Med. 2002; 166(2) : 159 -65 [DOI][PubMed]

  • 9.

    Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000; 342(19) : 1378 -84 [DOI][PubMed]

  • 10.

    Akselrod S, Gordon D, Ubel FA, Shannon DC, Berger AC, Cohen RJ. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science. 1981; 213(4504) : 220 -2 [PubMed]

  • 11.

    Bruser C, Winter S, Leonhardt S. Unsupervised heart rate variability estimation from ballistocardiograms. Proceedings of the 7th International Workshop on Biosignal Interpretation. 2012;

  • 12.

    Barron HV, Viskin S. Autonomic markers and prediction of cardiac death after myocardial infarction. Lancet. 1998; 351(9101) : 461 -2 [DOI][PubMed]

  • 13.

    Tsuji H, Larson MG, Venditti FJ, Manders ES, Evans JC, Feldman CL, et al. Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation. 1996; 94(11) : 2850 -5 [PubMed]

  • 14.

    Bigger JJ, Fleiss JL, Steinman RC, Rolnitzky LM, Kleiger RE, Rottman JN. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation. 1992; 85(1) : 164 -71 [PubMed]

  • 15.

    Abildstrom SZ, Jensen BT, Agner E, Torp-Pedersen C, Nyvad O, Wachtell K, et al. Heart rate versus heart rate variability in risk prediction after myocardial infarction. J Cardiovasc Electrophysiol. 2003; 14(2) : 168 -73 [PubMed]

  • 16.

    Dixon JB, Schachter LM, O'Brien PE, Jones K, Grima M, Lambert G, et al. Surgical vs conventional therapy for weight loss treatment of obstructive sleep apnea: a randomized controlled trial. JAMA. 2012; 308(11) : 1142 -9 [DOI][PubMed]

  • 17.

    Eckert DJ, White DP, Jordan AS, Malhotra A, Wellman A. Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. Am J Respir Crit Care Med. 2013; 188(8) : 996 -1004 [DOI][PubMed]

  • 18.

    Phillips CL, Grunstein RR, Darendeliler MA, Mihailidou AS, Srinivasan VK, Yee BJ, et al. Health outcomes of continuous positive airway pressure versus oral appliance treatment for obstructive sleep apnea: a randomized controlled trial. Am J Respir Crit Care Med. 2013; 187(8) : 879 -87 [DOI][PubMed]

  • 19.

    Roche F, Gaspoz JM, Court-Fortune I, Minini P, Pichot V, Duverney D, et al. Screening of obstructive sleep apnea syndrome by heart rate variability analysis. Circulation. 1999; 100(13) : 1411 -5 [PubMed]

  • 20.

    Kufoy E, Palma JA, Lopez J, Alegre M, Urrestarazu E, Artieda J, et al. Changes in the heart rate variability in patients with obstructive sleep apnea and its response to acute CPAP treatment. PLoS One. 2012; 7(3)[DOI][PubMed]

  • 21.

    Narkiewicz K, Montano N, Cogliati C, van de Borne PJ, Dyken ME, Somers VK. Altered cardiovascular variability in obstructive sleep apnea. Circulation. 1998; 98(11) : 1071 -7 [PubMed]

  • 22.

    Gula LJ, Krahn AD, Skanes A, Ferguson KA, George C, Yee R, et al. Heart rate variability in obstructive sleep apnea: a prospective study and frequency domain analysis. Ann Noninvasive Electrocardiol. 2003; 8(2) : 144 -9 [PubMed]

  • 23.

    Aydin M, Altin R, Ozeren A, Kart L, Bilge M, Unalacak M. Cardiac autonomic activity in obstructive sleep apnea: time-dependent and spectral analysis of heart rate variability using 24-hour Holter electrocardiograms. Tex Heart Inst J. 2004; 31(2) : 132 -6 [PubMed]

  • 24.

    Yang A, Schafer H, Manka R, Andrie R, Schwab JO, Lewalter T, et al. Influence of obstructive sleep apnea on heart rate turbulence. Basic Res Cardiol. 2005; 100(5) : 439 -45 [DOI][PubMed]

  • 25.

    Park DH, Shin CJ, Hong SC, Yu J, Ryu SH, Kim EJ, et al. Correlation between the severity of obstructive sleep apnea and heart rate variability indices. J Korean Med Sci. 2008; 23(2) : 226 -31 [DOI][PubMed]