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COST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006 SLEEP, AUTONOMIC CONTROL AND PSYCHOEMOTIONAL STATUS Giedrius Varoneckas Institute of Psychophysiology and Rehabilitation c/o Kaunas University of Medicine Vydūno Str. 4, Palanga LT-00135, Lithuania E-mail: [email protected] The goal of this presentation Demonstration of a diagnostic value of HR variability analysis as well as relationship between depression/anxiety, sleep quality, cardiovascular function and autonomic control From other hand, HR variability biofeedback training is powerful tool for treatment of this various disorders Background: What is Biofeedback? • Biofeedback is a treatment technique in which people are trained to improve their health by using signals from their own bodies • “Biofeedback" was coined in the late 1960s to describe laboratory procedures then being used to train experimental research subjects to alter brain activity, blood pressure, heart rate, and other bodily functions that normally are not controlled voluntarily • Psychologists use it to help tense and anxious clients learn to relax . Bette Runck DHHS Publication No (ADM) 83-1273 Background: Biofeedback & Autonomic Control • By providing access to physiological information about which the user is generally unaware, biofeedback allows users to gain control over physical processes previously considered automatic Jacenko M. Wikimedia Commons • Interaction between central nervous system and autonomic control plays role in “biofeedback” process • The biofeedback response is related to the baseline level of autonomic control Background: Autonomic Control Modifications • During wake-sleep cycle as a reflection of brain functions • In depression/anxiety • In somatic disorders (coronary artery disease) • In sleep restriction Autonomic HR control goes through three main mechanisms • balance between of sympathetic-parasympathetic branches of autonomic nervous system (HR frequency and oscillatory structure) • tonic control (HR variability), depending of P/S interaction • reflex control (mainly baroreflex) level might be drawn from assessment of baroreflex sensitivity and/or HR maximal response to AOT Sleep Electroencephalography Wakefulness (active) 50 mV 1 sec Wakefulness (passive) Stage 1 Theta waves Stage 2 Sleep spindles K-complex Stage 4 REM Sleep “Saw teeths ” Normal Sleep Histogram Sequences of States and Stages of Sleep on a Typical Night Identification and Staging of Adult Human Sleep. L.Shigley. Sleep Academic Award Heart Rate and Heart Rate Variability during Sleep Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 Methods of obtaining the HRV parameters may by divided into following groups: Time domain methods Spectral domain methods Non-linear methods Mathematic modeling methods HR analysis using power spectrum Three main oscillatory components: very low frequency component (VLFC) low frequency component (LFC) high frequency component (HFC) in absolute (ms) and relative (percent) values for evaluation: humoral, sympathetic-parasympathetic and parasympathetic control, correspondingly Heart rate analysis using Poincare plot RRr, difference on plot diagonal between minimal (RRmin) and maximal (RRmax) RR values RRt, maximal HR variability as maximal width-difference between of two points at parallel tangential lines determining plot RRmin RRmax P, square of the plot, representing overall HR variability RRmin, maximal HR frequency RRmax, HR frequency at its minimal level Methods Nonlinear analysis of continuous ECG during sleep: II. Dynamical measures Fell J. et al. Biol. Cybern. 82, 485-91 (2000) The correlation dimension serves as an estimator of the number of degrees of freedom in a system, this is, the number of variables required to generate the observed dynamics D2 - as a measure of the complexity of a time series (Grassberger & Proccacia 1983) ECG dynamics was considered to be composed of two aspects: (i) the inter-beat or RR variability; (ii) the PQRST complex Tab. Contrasts between sleep stages for the nonlinear ECG measures D2, L1, K2 and average first return time (p = 0.05) An increase in dominant chaoticity during REM sleep with regard to time-continuous nonlinear analysis is comparable to an increased heart rate variability The reduction in the correlation dimension (D2) may be interpreted as an expression of the withdrawal of respiratory influences during REM sleep D2 L1 K2 First return time I II n.s. n.s. n.s. n.s. I-SWS n.s. n.s. 0.022 n.s. I-REM 0.011 n.s. 0.011 0.026 II-SWS n.s. n.s. n.s. n.s. II-REM n.s. n.s. n.s. n.s. SWS-REM 0.0022 0.0006 n.s. 0.013 Methods: Detrended fluctuation analysis Comparison of detrended fluctuation analysis and spectral analysis for HRV in sleep and sleep apnea: 14 healthy subjects, 33 pts with moderate, and 31 pts with severe sleep apnea VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea Analysis Assignments for apnea severity sleep stage Spectral 69.7% 54.6% Discriminance 74.4% 85.0% Changes in HRV are better quantified by scaling analysis than by spectral analysis Penzel T. et al. IEEE Trans Biomed Eng. 2003;50(10):1143-51. Methods: Correlation dimension (D2) D2 Application of chaos theory in analyzing the HR in healthy subjects during sleep stages 4,00 3,00 2,00 W S1 S2 D2 non-trained subject S3 S4 REM The correlation between the changes in D2 during different sleep stages and the level of autonomic HR control was demonstrated well-trained sportsman The chaotic element of HR, expressed numerically by D2 depends on the baseline level autonomic HR control 4,00 3,50 3,00 2,50 2,00 1,50 1,00 W S1 Intact Atropine S2 Propranolol S3 S4 REM Propanolol + atropine Eidukaitis A. et al. Human Physiology, 2004, 30, 5, 551-5. The heart rate during synchronized sleep after different steps of heart denervation Experiment from unrestrained cats with chronically implanted electrodes Intact Baust W. & Bohnert B. The Regulation of Heart During Sleep Exp. Brain Res. 7, 169-180 (1969) Bilateral Stellatectomy Neurological Clinic, University of Düsseldorf Germany Bilateral Vagotomy Combined Stellatectomy & Vagotomy Changes in heart rate during shift from synchronize to desynchronized sleep Experiment from unrestrained cats with chronically implanted electrodes Intact Bilateral Vagotomy Combined Stellatectomy & Vagotomy Non-REM Sleep → REM Sleep Baust W. & Bohnert B. Exp. Brain Res. 7, 169-180 (1969) Sympathetic and parasympathetic control study Egberg & Katona modification of the model suggested by Rosenblueth & Simeone (Am J Physiol, 1934, 110, 42-55) Rs p intact HR s p R0 intrinsic HR s p R0 R sp HR before atropine administration p s R0 Rs HR after atropine administration s p R0 R sp HR before propranolol administration s p R0 Rp HR after propranolol administration Egberg JR and Katona PG, Am. J Physiology, 1980, 238, H829-H835. Sympathetic (S) and parasympathetic (P) multipliers’ values as functions of sleep S 1st night – adaptation 2nd night – intact 3rd night - atropine 0.025 mg/kg 4th night - propranolol retard 160 mg 5th night – atropine plus propranolol P Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 Poincare plots of RR intervals in healthy subject under different conditions of autonomic HR control Baseline Wakefulness Stage 2 Stage 3 REM Sleep Propranolol Atropine Propanolol & Atropine Heart rate, stroke volume, and cardiac output as a functions of sleep stages RR, s SV, ml 110 1 CO, l/min 7 90 5 1 70 50 1 RR, % 3 SV, % CO, % 120 130 130 110 110 110 100 90 90 90 70 70 W 1 2 3 4 REM W 1 2 3 4 REM W 1 2 3 4 REM Healthy Ss CAD Pts Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 & 290-298 Varoneckas G. Fiziologija Cheloveka, 1994, 20, 1, 76-83. Heart Rate Sleep Pattern in Healthy Subject W 1 2 3 4 REM Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 Poincare plots of RR intervals during individual sleep stages W before sleep Stages 1-2 Stages 3-4 REM Sleep Overall night Healthy Subject Typical HRSP Reduced HRSP Zemaityte D. et al. Biomedicine, 2001, 1, 1, 34-44 Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men Hélène Otzenberger, Claude Gronfier, Chantal Simon, Anne Charloux, Jean Ehrhart, François Piquard, and Gabrielle Brandenberger Laboratoire des Régulations Physiologiques et des Rythmes Biologiques chez l'Homme, Institut de Physiologie, 67085 Strasbourg Cedex, France Am J Physiol Heart Circ Physiol 1998, 275: H946-H950 Fig. 1. Examples of 5-min Poincaré plots with regard to power spectra of R-R intervals, according to sleep states: intrasleep awaking (W), stage 2 (St2), slow-wave sleep (SWS), and rapid eye movement sleep (REM). rRR, Interbeat autocorrelation coefficient of R-R intervals; R-Rn and R-Rn+1, successive R-R intervals; LF/HF, ratio of low- to high-frequency power. Heart Rate Sleep Pattern Typical Reduced Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 Zemaityte D. et al. Psychophysiology, 1984, 21(3), 290-298 Changes of HR power spectrum components impact during shifts of sleep stages 100 Healthy Ss - Typical HRSP Sveikieji 75 50 25 0 100 Tipinës CAD pts – Typical HRSP 75 50 25 0 100 CAD ptsRedukuotosios – Reduced HRSP 75 50 25 0 B W VLFC, % LM1 Stage 1 NLLDK, LFC, % LM2 % Stage 2 NLDK, HFC, % LM3 LM4 Stage 3 Stage 4 % NADK, % GM REM Zemaityte et al. Int J Psychophysiology, 1986, 4, 129-141 Effects of Aging and Cardiac Denervation on Heart Rate Variability During Sleep RR interval did not differ between young and older subjects during awake periods but was larger in the young subjects than in the older subjects during both non-REM and REM The total RR variability was higher in the young subjects than in the older volunteers during awake, non-REM sleep, and REM sleep Figure 1. RR during awake periods, non-REM, and REM sleep in normal young (open bars) and older (solid bars) subjects. Reduction in RR with aging is particularly evident during non-REM and REM sleep. +P<0.05, •P<0.01 vs old; ••P<0.01, XXP<0.001 vs awake. Crasset V. et al. Circulation. 2001;103:84 Effects of Aging and Cardiac Denervation on Heart Rate Variability During Sleep In conclusion, study reveals (1) that the normal, age-related disappearance of deep sleep impairs the nocturnal increase in cardiac vagal activity and (2) that nonautonomic mechanisms slightly increase HF oscillations in RR during non-REM sleep in humans. Figure 2. Effects of aging on RR variability during non-REM and REM sleep. HF oscillations in RR became more predominant than LF oscillations in RR during non-REM sleep in normal young subjects (open bars). These changes were lost with aging (solid bars). +P<0.05 vs old; ++P<0.05 vs awake. Crasset V. et al. Circulation. 2001;103:84 Vanoli E. et al. Heart Rate Variability in Specific Sleep Stages: A Comparison of Healthy Subjects and Patients After Myocardial Infarction. Circulation,1995; 91: 1918-1922. Spectral analyses of heart rate variability during non-rapid eye movement sleep from one normal individual and in a patient with a recent myocardial infarction Sleep contains information that is highly relevant to the identification of autonomic derangements associated with a higher risk for lethal events after MI. Expected surge in cardiac vagal activity associated with non-REM sleep is completely lost after MI. The higher risk for ischemic events and the unopposed sympathetic activity evident during REM sleep creates a condition in which lethal arrhythmic events are more likely to occur and provide new information to the understanding of sudden death at night. Poincare plots and power spectra of all-night HR recording Sportsman Healthy subject CAD patient Kesaite R..et al. Information Technology and Control, 2001, 2, 20-28 Non-trained healthy Ss Trained sportsmen The restorative function of sleep towards the cardiovascular system in trained sportsmen and non-trained healthy Ss 1,3 RR, ms SV ml 1,2 7 120 CO, l/min 50 RRB 2400 2000 6 40 1,1 5 1 30 4 3 40 0,8 W 1,2 1600 80 0,9 1 2 3 W 4 RR, ms 1 2 3 SV, ml 1,1 7 1200 20 W 4 1 2 3 800 4 CO, l/min. 50 RRB 6 110 2600 5 2000 4 0,9 30 1400 3 0,8 2 30 W 1 2 3 Sleep cycles Sleep stages: I 4 W 1 2 3 4 II III IV 800 20 W 1 2 Sleep cycles Sleep cycles TPR 40 1 70 TPR 3 4 Day Evening Morning REM Varoneckas G. Fiziologija Cheloveka, 1994, 20, 1, 76-83. HR variability in well-trained sportsman and non-trained subject well-trained sportsman non-trained subject Sleep AOT eveningtime AOT morningtime The restorative function of sleep towards the cardiovascular system in healthy Ss and CAD pts Healthy Ss 1,3 RR, ms 1,2 7 120 50 RRB 2000 40 1600 30 1200 20 800 TPR 5 80 1 4 0,9 3 40 0,8 1,2 CO, l/min 6 1,1 W CAD pts SV, ml 1 2 3 W 4 RR, ms 1 2 3 W 4 SV, ml 7 1 2 3 4 CO, l/min. 50 RRB 120 1,1 TPR 2600 40 2000 5 1 3200 80 30 1400 0,9 W 1 2 3 Sleep cycles Sleep stages: W 4 I 20 3 40 0,8 1 2 3 4 Sleep cycles II III W 1 2 Sleep cycles IV 3 800 4 Day-time Evening Morning REM Varoneckas G. Fiziologija Cheloveka, 1994, 20, 1, 76-83. HR variability during sleep and AOT in healthy subject and CAD patient CAD pt Healthy subject Sleep AOT evening-time AOT morning-time HR variability in CAD patient with and without HR restoration with HR restoration inability to restore Sleep AOT eveningtime AOT morningtime Total sleep time Healthy Subjects Pts with HR restoration CAD Patients Pts showing inability to restore HR min min 400 400 * 300 300 200 200 100 100 0 0 TST TST Sleep structure in CAD patients with Restoration of HR control Inability to restore HR min 200 * p<.05 150 100 * 50 * * 0 WASO Stage 1 Stage 2 Stage 3 Stage 4 REM HR variability in healthy subject and CAD pts surviving and died during 2-yr follow-up Healthy Survived Died Poincare maps from successive RR intervals during night sleep and exercise Zemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14. Poincare maps from successive RR intervals during sleep and exercise in cardiac pts with dysrrhythmias Healthy Ss SPT VTT PAF Zemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14 Prognostic value of ventricular arrhythmias and heart rate variability in patients with unstable angina Lanza GA et al. Heart. 2005 Dec 30 •17 cardiological centres in Italy •543 patients with UA and EF>40% •Holter ECG within 24 h of hospital admission •Adjusted for clinical (age, gender, cardiac risk factors, history of previous MI) and laboratory (troponin I, C-reactive protein, transient myocardial ischemia on HM) variables 3 HRV variables (standard deviation of RR intervals index, low frequency [LF] amplitude and low to high frequency ratio [LF/HF] ) were associated with in-hospital death, and bottom quartile values of most HRV variables predicted 6-month fatal events Prognostic value of ventricular arrhythmias and heart rate variability in patients with unstable angina Lanza GA et al. Heart. 2005 Dec 30 •17 cardiological centres in Italy •543 patients with UA and EF>40% •Holter ECG within 24 h of hospital admission •Adjusted for clinical (age, gender, cardiac risk factors, history of previous MI) and laboratory (troponin I, C-reactive protein, transient myocardial ischemia on HM) variables Table 2. Variables significantly predictive of in-hospital death at univariable analysis. Complex ventricular arrhythmias are not included as all patients who died showed these forms of arrhythmias on Holter monitoring. Odds ratio (95% CI) p Age >70 12.3 (1.5-10.12) 0.02 PVBs≥10/hr 7.25 (1.69-30.9) 0.007 SDNNi<39 ms 4.99 (1.18-21.1) 0.029 LF<15.7 ms 4.94 (1.16-20.9) 0.030 LF/FF<1.12 5.14 (1.21-21.9) 0.026 Complex VA and frequent PVBs were strongly predictive of death in hospital and at follow-up In UA patients with preserved myocardial function, both VA and HRV are independent predictors of in-hospital and medium-term mortality HR variability in evaluation of autonomic control and cardiovascular status in patients after cardiac surgery Before CABG surgery After 1 mos After 6 mos Poincare maps from successive RR intervals during sleep and exercise in cardiac patient Zemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14 Impairment of cardiovascular autonomic control in patients early after cardiac surgery 25 male patients underwent CABG surgery normal values were obtained from healthy volunteers Baroreflex function, HR variability and blood pressure variability in patients early after coronary surgery Obviously, there is a vagal suppression 20 h after surgery, while the sympathetic tonus works in a normal range This unbalanced interaction of the autonomous systems is similar to findings in patients after myocardial infarction Bauernschmitt R. et al. Eur J Cardiothorac Surg. 2004;25(3):320-6. Modification of HR variability due to medication by ACEI Before After 1 mo After 12 mos Poincare maps from successive RR intervals during night sleep and exercise Changes of total HR variability during night sleep due to ACEI treatment 100 p < .05 RR, ms 80 60 during sleep 40 20 0 60 0 B 1 3 6 12 mos RR, ms p < .05 40 p < .05 p < .05 after sleep p < .05 before sleep 20 0 0 B 1 6 12 mos Gujjar AR et al. Heart rate variability and outcome in acute severe stroke: role of power spectral analysis. Neurocrit Care. 2004;1(3):347-53. •25 patients: 11 died, 10 - poor, and 4 - good outcome •6 comatose patients (Glasgow Coma score<9 at admission) 16 had focal weakness all had abnormal brain computed tomography •HRV parameters from continuous 1-hour record •Low-frequency (LF) and very low-frequency (VLF) spectral power correlated with mortality HRV measurements are independent predictors of outcome in acute severe stroke Heart rate, systolic and diastolic blood pressure and baroreflex sensitivity during sleep Baroreflex sensitivity, 1100 heart rate and blood 1000 pressure during sleep RR ms 900 800 150 mmHg Healthy Ss 125 n=16 100 Systolic BP Diastolic BP 75 CAD pts 50 n=48 25 20 BRS ms/mmHg 15 10 5 W 1 2 3-4 REM Baroreflex sensitivity during different sleep stages in CAD patients with sleep apnea 16 BRS ms/mmHg 12 8 4 W 1 AHI5 n=59 2 3-4 5>AHI15 n=29 REM AHI>15 n=31 The groups were matched according age, body mass index and cardiac pathology 1200 Baroreflex sensitivity, heart rate and blood pressure during sleep in CAD patients RR ms 1100 1000 900 150 Systolic BP mmHg 125 Without VPBs (57 pts) With VPBs (62 pts) 100 Diastolic BP 75 50 The groups were matched according age, body mass index and cardiac pathology 25 15 BRS ms/mmHg 10 5 W 1 2 3-4 REM Baroreflex sensitivity during sleep stages CAD patients with severe sleep apnea CAD patients without sleep apnea BRS BRS 15 ms/mmHg 15 10 10 5 5 0 W 1 2 3-4 Without VPBs REM 0 ms/mmHg W 1 With VPBs 2 3-4 REM Baroreflex sensitivity before and after supraventricular paroxysmal tachycardia (SVPT) SVPT BRS 12 SVPT WASO – 7 Stage 1 – 2 Stage 2 – 6 REM –2 ms/mmHg 8 4 N=17 4 3 2 SPVT ECG 2 3 4 min. Polysomnography Wakefulness Body movemts REM Sleep Stage 1 Stage 2 Stage 3 Stage 4 Concluding • HR variability, measured during sleep, reflects autonomic HR control and is dependent on the subject’s functional status • HR variability changes during sleep reflect the restoration of autonomic HR control and correlate to the HR responses to orthostatic test • HR variability analysis during sleep is a valuable tool for clinical diagnostics Psychoemotional status (Depression, Anxiety) Sleep disturbances (Insomnia) Cardiovascular pathology Depression & Coronary artery disease After myocardial infarction (Major Depression 15-20% ) (Burg et Abrams, 2001) Increased mortality from CAD (50%) (Pennix et al., 2001) Increased mortality in HF pts (16.2% after 1 yr) (Jinag et al., 2001) Increased risk for development of HF (Williams et al., 2002) Increased mortality after bypass surgery (Baker et al., 2001) Increased mortality after heart transplantation (Zipfel et al., 2002) Predictor for CAD in healthy Ss (Metaanalysis of 11 trials) (Rugulies 2002) Sleep more active than passive process Psychoemotional status (Depression, Anxiety) Sleep disturbances (Insomnia) Cardiovascular pathology Depression & Coronary artery disease (Lambert 2002): Altered platelet function Reduced parasympathetic control Increased catecholamine levels Abnormal inflammatory responses Abnormalities in thyroidal function Increased risk factors (smoking, overweight, less prone to exercise, to enroll rehabilitation programme etc) Cardiotoxicity of antidepressants (TCAs) Sleep more active than passive process Contingent (I) 56 healthy subjects 1335 Coronary artery disease patients: NYHA I (64), II (758), III (513) CAD patients: without complications 270 with Hypertension 304 with Congestive Heart failure 648 with CAD and Diabetes 113 Sleep quality in healthy subjects and CAD patients CAD Patients Healthy Subjects CAD Patients I II 352.2 318.1* 335.8 319.7 90.7 85.9* 89.1 86.4 84.9*I, II 120.6 92.9* 101.9 89.0 97.7 9.3 14.1* 10.9 13.6 15.1*I; II 12.9 12.2 13.3 12.9 10.9*I; II S1, % 7.8 9.5 9.4 9.6 9.4 S2, % 50.6 53.1 52.3 52.8 53.9 S3, % 11.0 7.0* 9.6 6.9*I 6.8*I S4, % 6.1 1.3* 2.1 1.3 1.1*I BM, % 2.4 2.7 2.4 2.8 2.7 PSQI 5.2 7.8* 5.9* 7.2*I 9.0*I; II TST, min. SE, % REM lat., min. WASO, % REM Sleep, % NYHA Class III 313.8*I * p< .05 Sleep quality in healthy subjects and CAD patients distributed according prevalence of hypertension, heart failure and diabetes CAD Patients CAD Hypertension Heart failure 1 2 3 4 352.2 326.4* 326.6* 317.1* 308.0* 90.7 87.4 88.0 84.1* 86.3* REM lat., min. 120.6 92.5* 88.1* 90.8 88.1* WASO, % 9.3 12.6 12.0 15.9* 13.7* 1:3, 2:3 12.9 13.8 13.4 11.8 11.8 1:3, 1:4, 2:3 S1, % 7.8 8.5 11.0* 9.1 10.4 1:2, 2:3 S2, % 50.6 52.5 51.6 52.8 55.4* S3, % 11.0 7.9* 7.4* 6.7* 4.9* S4, % 6.1 1.9* 1.7* 1.1* 0.65* 1:3, 1:4 BM, % 2.4 2.8 2.9 2.6 3.1 3:4 PSQI 5.2 7.1* 6.5 8.2* 8.8* 1:3, 1:4, 2:3, 2:4 Healthy Subjects TST, min. SME, % REM Sleep, % Diabetes p 1:3, 2:3 1:4, 3:4 Randomization of 970 CAD pts according clinical status NYHA Functional Class Heart Failure p 1 Angina Pectoris (Class) p 2 0 1 2 p I II III 0 3 Without Anxiety and Depression 9 47 48 46 28 30 With Anxiety 7 40 40 0.03 0.99 35 34 18 3.59 0.17 8 18 55 6 6.53 0.09 With Depression 3 16 17 0.01 0.99 12 14 10 2.05 0.36 8 With Anxiety and Depression 2 40 51 4.51 0.11 33 30 30 1.59 0.45 7 15 57 14 12.3 0.01 16 33 47 8 6 18 4 3.41 0.33 Sleep quality in CAD patients’ groups distributed according Anxiety and Depression CAD Patients Without Anxiety and Depression With Anxiety With Depression 1 2 3 TST, min. With Anxiety and Depression p 4 322.5 314.6 304.3 316.5 1:3 SE, % 86.4 86.5 84.9 84.7 REM lat., min. 92.9 90.2 95.0 95.3 WASO, % 13.6 13.5 15.1 15.3 REM Sleep, % 12.8 12.4 10.3 10.6 S1, % 9.3 9.6 11.2 10.1 S2, % 52.9 53.0 55.3 54.2 S3, % 7.3 7.4 4.5 5.9 1:3, 1:4, 2:3, 2:4 S4, % BM, % 1.4 2.7 1.4 2.6 0.5 3.1 1.2 2.8 1:3, 2:4 PSQI 6.5 8.7 7.4 10.7 1:3, 1:4, 2:3 1:2, 1:4, 2:3, 2:4, 3:4 1100 82 1000 80 900 78 σRR RR HR rate and HR variability in different groups of CAD pts according to Anxiety and Depression 800 76 700 74 600 72 500 Without anxiety and depression With anxiety With depression With anxiety and depression * * * 70 Without With anxiety With With anxiety anxiety and depression and depression depression * - p<.05 Maximal HR response to active orthostasis at evening and morning-time in CAD patients Evening-time Morning-time p < 0.0001 p < 0.0001 p > 0.05 With Without With 35 30 25 20 15 p < 0.01 10 5 0 Without Anxiety Depression Poincare plots of RR interval for the CAD patients without depression with depression HR variability analysis Healthy Ss CAD pts Personality traits and heart rate variability predict longterm cardiac mortality after myocardial infarction Clara Carpeggiani, Michele Emdin, Franco Bonaguidi, Patrizia Landi, Claudio Michelassi, Maria Giovanna Triv ella, Alberto Macerata, and Antonio L’Abbate European Heart Journal, 2005, 26, 16, 1612-1617. Personality traits and heart rate variability predict longterm cardiac mortality after myocardial infarction Clara Carpeggiani, Michele Emdin, Franco Bonaguidi, Patrizia Landi, Claudio Michelassi, Maria Giovanna Triv ella, Alberto Macerata, and Antonio L’Abbate European Heart Journal, 2005, 26, 16, 1612-1617. Table 4. Univariate and multivariable Cox proportional regression analyses HR (95% CI) P-value I (<4) 3.41 (1.59-7.30) 0.037 HF power (<221 ms2) 3.08 (1.44-6.50) 0.003 pNN50 (<5%) 2.77 (1.06-7.23) 0.021 O (<5) 2.32 (1.13-4.77) 0.022 RR interval (<870 ms2) 2.25 (1.03-4.92) 0.033 LF power (<148 ms2) 2.21 (1.06-4.59) 0.031 HF power (<221 ms2) I (<4) 2.76 (1.28-5.92) 4.18 (1.88-9.26) 0.003 0.007 Hospital length of stay (>15 days) 2.60 (1.23-5.52) 0.040 Non-Q vs. Q 3.18 (1.35-7.46) 0.014 Predictors of death Univariate model Multivariable model Concluding - I CAD pts demonstrated significantly reduced TST, SE, SWS & REM sleep and increased WASO Worsening of functional class of CAD pts was paralleled by decreased quality of sleep, measured by objective and subjective parameters, as well as quality of life Anxiety was related more to worsening subjective sleep quality, while depression – to changes in sleep structure and reduced total sleep time Concluding - II • Restoration of cardiovascular function during sleep was related not only to TST, however with SWS • Decreased sympathetic input was observed in pts with anxiety, while reduction of both, sympathetic and parasympathetic one, was characteristic to pts with depression • CAD pts with depression and anxiety might be seen as loosing ability for restoration of autonomic control of HR ? Biofeedback & Treatment • Coronary artery disease • Insomnia • Depression & Anxiety Biofeedback: Main mechanism • Biofeedback training increases the amplitude of RSA (max. increases the amplitude of HR oscillations only at breathing rate approx. 0.1 Hz) • People slow their breathing to this rate to a point where resonance occurs between respiratory-induced oscillations (RSA) • Oscillations that naturally occur at this rate, triggered in part by baroreflex • Biofeedback exercises the baroreflexes, and renders them more efficient Lehrer P. M. et al. Resonant Frequency Biofeedback Training to Increase Cardiac Variability: Rationale and Manual for Training, Applied Psychophysiology and Biofeedback, 25, 3, 9/1/2000, Pages 177-191 • An increase in vagal control: Acute increases in low-frequency and total spectrum heart rate variability, and in vagal baroreflex gain, correlated with slow breathing during biofeedback periods Lehrer P. M. et al. Heart Rate Variability Biofeedback Increases Baroreflex Gain and Peak Expiratory Flow. Psychosomatic Medicine 65:796-805 (2003) Heart Rate Reactions at Paced Breathing Vaschillo E. et al. Characteristics of Resonance in Heart Rate Variability Stimulated by Biofeedback, Applied Psychophysiology and Biofeedback, 31, 2, 2006, Pages 129-142. Increased Baroreflex Gain during BFB Lehrer P. M. et al. Heart Rate Variability Biofeedback Increases Baroreflex Gain and Peak Expiratory Flow. Psychosomatic Medicine 65:796-805 (2003). Biofeedback and HR Variability Resonant frequency HR variability biofeedback increases baroreflex gain and peak expiratory flow in healthy individuals and has positive effects in treatment of asthma patients Lehrer P. et al. Respiratory Sinus Arrhythmia Biofeedback Therapy for Asthma: A Report of 20 Unmedicated Pediatric Cases Using the Smetankin Method, Applied Psychophysiology and Biofeedback, 25, 3, 9/1/2000, Pages 193-200. Biofeedback and Baroreflex • 5 healthy Ss learned to control oscillations in HR using biofeedback training to modify their HR variability at 7 frequencies within the range of 0.01–0.14 Hz • The highest oscillation amplitudes were produced in the range of 0.055– 0.11 Hz for HR and 0.02–0.055 Hz for BP • High and low target-frequency oscillation amplitudes at specific frequencies could be explained by resonance among various oscillatory processes in the cardiovascular system • The exact resonant frequencies differed among individuals • Changes in HR oscillations could not be completely explained by changes in breathing. The biofeedback method also allowed to quantity characteristics of inertia, delay & speed sensitivity in baroreflex system. • HRV biofeedback can be used in diagnostics of various autonomic and cardiovascular disorders as well as for treating these disorders Vaschillo E. et al. Heart Rate Variability Biofeedback as a Method for Assessing Baroreflex Function: A Preliminary Study of Resonance in the Cardiovascular System, Applied Psychophysiology and Biofeedback, 27, 1, 3/1/2002, Pages 1-27. Biofeedback & Baroreflex Sensitivity • 32 students, 3 biofeedback sessions with four 5-min trials each, in which they had to increase and decrease baroreflex • BRS was assessed on-line using a noninvasive spontaneous sequence method in the time domain • The increase in BRS during the Increase Condition was associated with a significant reduction in blood pressure and increase in heart period • The opposite changes were observed during the Decrease Condition • The study demonstrates the modification of the baroreflex function through biofeedback Reyes del Paso G. A. & González Ma. I. Modification of Baroreceptor Cardiac Reflex Function by Biofeedback, Applied Psychophysiology and Biofeedback, 29, 3, 9/1/2004, Pages 197-211. Biofeedback of the R-wave-to-pulse interval (RPI) • Biofeedback of the R-wave-to-pulse interval, a measure related to the pulse wave velocity, enables participants with either high or low arterial blood pressure to modify their blood pressure • 12 pts with high blood pressure (mean BP = 142.6/99.9 mmHg; and 10 pts with low blood pressure (mean BP = 104.8/73.2) received 3 individual sessions of RPI biofeedback within a 2week period • Pts with high BP achieved significant reductions of systolic (15.3 mmHg) and diastolic (17.8 mmHg) BP levels from the beginning of the first to the end of the last training session. • In contrast, pts with low BP achieved significant increases in systolic (12.3 mmHg) and diastolic (8.4 mmHg) BP levels Rau H. et al. Biofeedback of R-Wave-to-Pulse Interval Normalizes Blood Pressure, Applied Psychophysiology and Biofeedback, 28, 1, 3/1/2003, Pages 37-46. Biofeedback & Heart Rate Variability 20 COPD patients participated in heart rate variability (HRV) biofeedback (5 weekly sessions) and walking with pulse oximetry feedback (4 weekly sessions) After 10 weeks of training, participants showed statistically and clinically significant improvements in 6MWD and quality of life. Significant changes were also seen in self-efficacy, disability, dyspnea before and after the 6MWD, and HRV amplitude during spontaneous breathing. Giardino N. D., Chan L., Borson S. Combined Heart Rate Variability and Pulse Oximetry Biofeedback for Chronic Obstructive Pulmonary Disease: Preliminary Findings, Applied Psychophysiology and Biofeedback, 29, 2, 6/1/2004, Pages 121-133. Biofeedback and Heart Rate Variability These results tentatively suggest that the biofeedback method can produce longterm changes in multiple organ systems that are affected by autonomic control Heart Rate Variability Biofeedback Application • To treat autonomic dysfunction with a variety of clinical manifestations, including anxiety and high BP Chernigovskaya N. V. et al. Voluntary control of the heart rate as a method of correcting the functional state in neurosis. Human Physiology, 1990; 16: 58–64. • To reduce heart rate (HR) response to mental stress following HR feedback training Goodie J.L. & Larkin K. T. Changes in Hemodynamic Response to Mental Stress with Heart Rate Feedback Training, Applied Psychophysiology and Biofeedback, 26, 4, 12/1/2001, Pages 293-309. • To treat asthma patients Lehrer P. et al. Respiratory Sinus Arrhythmia Biofeedback Therapy for Asthma: A Report of 20 Unmedicated Pediatric Cases Using the Smetankin Method, Applied Psychophysiology and Biofeedback, 25, 3, 9/1/2000, Pages 193-200. • To increase vagal tone in cardiac patients and improve their functional status • To enhance professional skills • etc … Heart Rate Variability • Powerful diagnostic tool • Useful for treatment of various disorders using biofeedback Acknowledgement • Prof. Danguolė Žemaitytė, M.D., D. Sci. (habil.) • Audrius Alonderis, M.D. • Julija Brožaitienė, M.D., D. Sci. (habil.) • Inga Duonelienė, M.D., D. Sci. • Andrejus EIdukaitis, D. Sci. • Vaidutė Gelžinienė, M.D., D. Sci. • Ramutė Kėsaitė, D. Sci • Arvydas Martinkėnas, D. Sci. • Aurelija Podlipskytė, Dip. Eng., D. Sci. • Gražina Valytė, M.D. • Linas Zakarevicius, Dip. Eng. • Geriuldas Žiliukas, M.D., D. Sci. (habil.) Thank you for attention