SLEEP, AUTONOMIC CONTROL AND PSYCHOEMOTIONAL STATUS Giedrius VaroneckasInstitute of Psychophysiology and Rehabilitationc/o Kaunas University of MedicineVydno Str. 4, Palanga LT-00135, LithuaniaE-mail: giedvar@ktl.mii.ltCOST B27 ENOC Joint WGs Meeting Swansea UK, 16-18 September 2006

Demonstration of a diagnostic value of HR variability analysis as well as relationship between depression/anxiety, sleep quality, cardiovascular function and autonomic controlThe goal of this presentation 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 bodiesBiofeedback" 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 voluntarilyPsychologists use it to help tense and anxious clients learn to relax Bette Runck. DHHS Publication No (ADM) 83-1273

Background: Biofeedback & Autonomic ControlBy providing access to physiological information about which the user is generally unaware, biofeedback allows users to gain control over physical processes previously considered automatic

Interaction between central nervous system and autonomic control plays role in biofeedback processThe biofeedback response is related to the baseline level of autonomic controlJacenko M. Wikimedia Commons

Background: Autonomic Control ModificationsDuring wake-sleep cycle as a reflection of brain functions In depression/anxietyIn somatic disorders (coronary artery disease)In sleep restriction

Autonomic HR control goes through three main mechanismsbalance between of sympathetic-parasympathetic branches of autonomic nervous system (HR frequency and oscillatory structure)tonic control (HR variability), depending of P/S interactionreflex control (mainly baroreflex) level might be drawn from assessment of baroreflex sensitivity and/or HR maximal response to AOT

Sleep Electroencephalography K-complex

Normal Sleep HistogramSequences of States and Stages of Sleep on a Typical NightIdentification 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

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, correspondinglyHR analysis using power spectrum

Heart rate analysis using Poincare plotRRrRRminRRmaxRRrtP, square of the plot, representing overall HR variability

RRr, difference on plot diagonal between minimal (RRmin) and maximal (RRmax) RR valuesRRt, maximal HR variability as maximal width-difference between of two points at parallel tangential lines determining plotRRmin, maximal HR frequencyRRmax, 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 dynamicsD2 - as a measure of the complexity of a time series (Grassberger & Proccacia 1983) 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 sleepECG dynamics was considered to be composed of two aspects: (i) the inter-beat or RR variability; (ii) the PQRST complexTab. Contrasts between sleep stages for the nonlinear ECG measures D2, L1, K2 and average first return time (p = 0.05)

Methods: Detrended fluctuation analysis

Penzel T. et al. IEEE Trans Biomed Eng. 2003;50(10):1143-51.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 Changes in HRV are better quantified by scaling analysis than by spectral analysisVLF, 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

Methods: Correlation dimension (D2) Application of chaos theory in analyzing the HR in healthy subjects during sleep stagesThe correlation between the changes in D2 during different sleep stages and the level of autonomic HR control was demonstratedThe chaotic element of HR, expressed numerically by D2 depends on the baseline level autonomic HR controlEidukaitis A. et al. Human Physiology, 2004, 30, 5, 551-5.

Chart1

2.71989100820.05003470310.05003470313.22240.17255209470.1725520947

2.85254716980.05538492980.05538492983.40857142860.22050767610.2205076761

3.00189164370.03079335630.03079335633.36529411760.1268637420.126863742

3.20257281550.05517491580.05517491583.44816326530.1387643050.138764305

3.38153846150.08476959450.08476959453.57571428570.24922635470.2492263547

2.80645348840.04805868810.04805868813.38078947370.15197712980.1519771298

non-trained subject

well-trained sportsman

D2

Sveiki+Sport

well-trained sportsman

Budr.Stadija1Stadija2Stadija3Stadija4GM

N252868492138

Mean3.223.413.373.453.583.38

Std Dev0.440.600.530.500.580.48

Conf Int0.170.220.130.140.250.15

non-trained subject

WS1S2S3S4REM

N3673181089412156344

Mean2.722.853.003.203.382.81

Std Dev0.490.500.520.570.540.45

Conf Int0.050.060.030.060.080.05

Sveiki+Sport

00.05003470310.050034703100.17255209470.1725520947

00.05538492980.055384929800.22050767610.2205076761

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00.05517491580.055174915800.1387643050.138764305

00.08476959450.084769594500.24922635470.2492263547

00.04805868810.048058688100.15197712980.1519771298

non-trained subject

well-trained sportsman

D2

Chart3

3.11352.79643.48352.49

2.713752.79366666673.3282.628

2.91221428572.99058333333.509252.9714166667

3.51657142863.10585714293.84533333332.7835

3.673252.95253.7912.6205

2.68177777782.8412.53823529412.4395

Intact

Atropine

Propranolol

Propanolol + atropine

D2

Vaistai

D2

IntactWS1S2S3S4REM

Kontrol3.112.712.913.523.672.68

Atropine

Atropinas2.802.792.993.112.952.84

Propranolol

Propranololas3.483.333.513.853.792.54

Propanolol + atropine

2.492.632.972.782.622.44

BIIIIIIIVGM

Vaistai

0000

0000

0000

0000

0000

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Intact

Atropine

Propranolol

Propanolol + atropine

D2

0000

0000

0000

0000

0000

0000

Kontrol

Atropinas

Propranololas

Miego stadijos

D2

The heart rate during synchronized sleep after different steps of heart denervation Experiment from unrestrained cats with chronically implanted electrodesIntactBilateral StellatectomyBilateral VagotomyCombined Stellatectomy& VagotomyBaust W. & Bohnert B. The Regulation of Heart During Sleep Exp. Brain Res. 7, 169-180 (1969)Neurological Clinic, University of Dsseldorf Germany

IntactBilateral VagotomyCombined Stellatectomy& VagotomyChanges in heart rate during shift from synchronize to desynchronized sleep Experiment from unrestrained cats with chronically implanted electrodesBaust W. & Bohnert B. Exp. Brain Res. 7, 169-180 (1969)Non-REM Sleep REM Sleep

Sympathetic and parasympathetic control study

Egberg JR and Katona PG, Am. J Physiology, 1980, 238, H829-H835. Egberg & Katona modification of the model suggested by Rosenblueth & Simeone (Am J Physiol, 1934, 110, 42-55)

Sympathetic (S) and parasympathetic (P) multipliersvalues as functions of sleepZemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 1st night adaptation2nd night intact3rd night - atropine 0.025 mg/kg 4th night - propranolol retard 160 mg 5th night atropine plus propranololSP

Poincare plots of RR intervals in healthy subject under different conditions of autonomic HR control

WakefulnessStage 2Stage 3REM SleepAtropineBaselinePropranololPropanolol & Atropine

Heart rate, stroke volume, and cardiac output as a functions of sleep stagesRR, sSV, mlCO, l/min W 1 2 3 4 REMRR, %SV, %CO, % W 1 2 3 4 REM W 1 2 3 4 REMHealthy SsCAD PtsZemaityte 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 SubjectZemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 W 1 2 3 4REM

Poincare plots of RR intervals during individual sleep stagesW before sleepStages 1-2Stages 3-4REM SleepOverall nightHealthySubjectTypical HRSPReduced HRSPCAD PatientsZemaityte D. et al. Biomedicine, 2001, 1, 1, 34-44

Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men Hlne Otzenberger, Claude Gronfier, Chantal Simon, Anne Charloux, Jean Ehrhart, Franois Piquard, and Gabrielle Brandenberger Fig. 1. Examples of 5-min Poincar 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. Laboratoire des Rgulations Physiologiques et des Rythmes Biologiques chez l'Homme, Institut de Physiologie, 67085Strasbourg Cedex, FranceAm J Physiol Heart Circ Physiol1998, 275: H946-H950

Heart Rate Sleep Pattern Zemaityte D. et al. Psychophysiology, 1984, 21(3), 279-289 Zemaityte D. et al. Psychophysiology, 1984, 21(3), 290-298 ReducedTypical

Changes of HR power spectrum components impact during shifts of sleep stagesHealthy Ss - Typical HRSPCAD pts Typical HRSPCAD pts Reduced HRSP W Stage 1 Stage 2 Stage 3 Stage 4 REMVLFC, %LFC, %HFC, %Zemaityte et al. Int J Psychophysiology, 1986, 4, 129-141

Sveikieji

0

25

50

75

100

Effects of Aging and Cardiac Denervation on Heart Rate Variability During SleepCrasset V. et al. Circulation. 2001;103:84Figure 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

Effects of Aging and Cardiac Denervation on Heart Rate Variability During SleepCrasset V. et al. Circulation. 2001;103:84Figure 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

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 infarctionSleep 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 subjectCAD patientKesaite R..et al. Information Technology and Control, 2001, 2, 20-28

The restorative function of sleep towards the cardiovascular system in trained sportsmen and non-trained healthy Ss Trained sportsmenNon-trained healthy SsVaroneckas G. Fiziologija Cheloveka, 1994, 20, 1, 76-83.

HR variability in well-trained sportsman and non-trained subject

well-trained sportsman

non-trained subject

SleepAOT evening-time AOT morning-time

The restorative function of sleep towards the cardiovascular system in healthy Ss and CAD ptsCAD ptsHealthy SsVaroneckas G. Fiziologija Cheloveka, 1994, 20, 1, 76-83.

HR variability during sleep and AOT in healthy subject and CAD patient CAD ptHealthy subject

HR variability in CAD patient with and without HR restoration

with HR restoration

inability to restore

SleepAOT evening-time AOT morning-time

Total sleep timeminmin Healthy Subjects Pts with HR restoration CAD Patients Pts showing inability to restore HR *

min** Restoration of HR control Inability to restore HR*Sleep structure in CAD patients with* p

HR variability in healthy subject and CAD pts surviving and died during 2-yr follow-up Healthy SurvivedDiedPoincare maps from successive RR intervals during night sleep and exerciseZemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14.

Poincare maps from successive RR intervals during sleep and exercise in cardiac pts with dysrrhythmiasHealthy SsSPTVTTPAFZemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14

Prognostic value of ventricular arrhythmias and heart rate variability in patients with unstable anginaLanza GA et al. Heart. 2005 Dec 30 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 events17 cardiological centres in Italy543 patients with UA and EF>40%Holter ECG within 24 h of hospital admissionAdjusted for clinical (age, gender, cardiac risk factors, history of previous MI) and laboratory (troponin I, C-reactive protein, transient myocardial ischemia on HM) variables

Prognostic value of ventricular arrhythmias and heart rate variability in patients with unstable anginaLanza GA et al. Heart. 2005 Dec 30 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 17 cardiological centres in Italy543 patients with UA and EF>40%Holter ECG within 24 h of hospital admissionAdjusted for clinical (age, gender, cardiac risk factors, history of previous MI) and laboratory (troponin I, C-reactive protein, transient myocardial ischemia on HM) variablesTable 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)pAge >7012.3 (1.5-10.12)0.02PVBs10/hr7.25 (1.69-30.9)0.007SDNNi

HR variability in evaluation of autonomic control and cardiovascular status in patients after cardiac surgeryBefore CABG surgeryAfter 1 mosAfter 6 mosPoincare maps from successive RR intervals during sleep and exercise in cardiac patientZemaityte D. et al. Biomedicine, 2002, 2, 1, 2-14

Impairment of cardiovascular autonomic control in patients early after cardiac surgeryBauernschmitt R. et al. Eur J Cardiothorac Surg. 2004;25(3):320-6. Obviously, there is a vagal suppression 20 h after surgery, while the sympathetic tonus works in a normal rangeThis unbalanced interaction of the autonomous systems is similar to findings in patients after myocardial infarction25 male patients underwent CABG surgery normal values were obtained from healthy volunteersBaroreflex function, HR variability and blood pressure variability in patients early after coronary surgery

Modification of HR variability due to medication by ACEIBeforeAfter 1 moAfter 12 mosPoincare maps from successive RR intervals during night sleep and exercise

Changes of total HR variability during night sleep due to ACEI treatmentbefore sleepafter sleepRR, msp < .05sRR, msp < .05p < .05p < .05p < .05during sleep

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.HRV measurements are independent predictors of outcome in acute severe stroke25 patients: 11 died, 10 - poor, and 4 - good outcome 6 comatose patients (Glasgow Coma score

Heart rate, systolic and diastolic blood pressure and baroreflex sensitivity during sleep

Baroreflex sensitivity, heart rate and blood pressure during sleep

Healthy Ssn=16CAD ptsn=48

Chart5

12.13.233.249.030.890.89

154.364.288.550.820.82

13.93.453.428.470.740.75

13.433.883.898.050.950.94

11.83.493.467.760.650.66

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| | | | Confid. | Confid. | | | |sv

| Variable | Valid N | Mean | -95,000% | +95,000% | Minimum | Maximum | Std.Dev. |

+----------+----------+----------+----------+----------+----------+----------+----------+

| BRJV1 | 112 | 8,554821 | 7,734264 | 9,375378 | 1,690000 | 26,55000 | 4,382370 |12.1VAR4VAR5

| BRJV2 | 122 | 8,473033 | 7,727360 | 9,218705 | 2,430000 | 25,32000 | 4,160205 |1513.233.24

| BRJV3 | 85 | 8,046353 | 7,099281 | 8,993425 | 1,630000 | 26,77000 | 4,390791 |13.924.364.28

| BRJV5 | 108 | 7,762130 | 7,108362 | 8,415898 | 1,900000 | 22,01000 | 3,427266 |13.4333.453.42

| BRJV6 | 119 | 9,034538 | 8,146628 | 9,922448 | 2,180000 | 29,27000 | 4,891222 |11.843.883.89

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106.24.84.8

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592.92.2

60.53.73.7

60.13.33.3

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02.92.2078

03.73.7066

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W 1 2 3-4 REM

0101003.53.5

08904.14

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W 1 2 3-4 REM

0101003.53.5

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02.92.2078

03.73.7066

03.33.3055

04.54.3065

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W 1 2 3-4 REM

0101003.53.500782.92.2

08904.1400663.73.7

06603.73.200553.33.3

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W 1 2 3-4 REM

0101003.53.500782.92.2

08904.1400663.73.7

06603.73.200553.33.3

07704.84.800654.54.3

07804.14.100663.83.9

Baroreflex sensitivity during different sleep stages in CAD patients with sleep apneaAHI5n=595>AHI15n=29AHI>15n=31 ms/mmHg BRSThe groups were matched according age, body mass index and cardiac pathology

Chart1

10.21.71.38.97.81.9133281.520221.9209170.8154

91.3197.11.7719892.187371.3901181.055718

91.21.197.21.7911171.886981.2911371.258724

8.51.21.38.96.92.6947093.208390.80.757737

8.110.98.56.81.87741.73580.8801091.037801

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01.71.3001.9133281.520221.9209170.8154

01.31001.7719892.187371.3901181.055718

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010.9001.87741.73580.8801091.037801

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2

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111.339110.161110.855.0515.0517.10567.1056.5386.539

107.961109.751118.0134.4174.4178.90148.90110.780510.781

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103.761102.362116.325.5145.51310.700910.713.726213.726

111.339111.503121.2465.0515.0518.45348.45410.082510.083

59.759.857.13.83.96.16.27.37.3

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5656.364.74.54.588.115.215.1

60.363.864.14.64.67.77.710.610.6

110.856.5386.539

118.01310.780510.781

114.3358.69528.696

116.3213.726213.726

121.24610.082510.083

05.0515.05107.10567.10506.5386.539

04.4174.41708.90148.901010.780510.781

04.2844.28407.29547.29508.69528.696

05.5145.513010.700910.7013.726213.726

05.0515.05108.45348.454010.082510.083

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01107.10567.10506.5386.539

008.90148.901010.780510.781

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0010.700910.7013.726213.726

008.45348.454010.082510.083

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03.93.906.36.409.59.6

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W 1 2 3-4 REM

Without VPBs (57 pts)With VPBs (62 pts)Baroreflex sensitivity, heart rate and blood pressure during sleep in CAD patientsThe groups were matched according age, body mass index and cardiac pathology

Without VPBsWith VPBsBaroreflex sensitivity during sleep stagesCAD patients with severe sleep apneaCAD patients without sleep apnea

4 3 2 2 3 4 min.SVPT ms/mmHg Baroreflex sensitivity before and after supraventricular paroxysmal tachycardia (SVPT)BRSSPVTECGSVPTWASO 7Stage 1 2Stage 2 6REM 2

N=17

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PolysomnographyWakefulness 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 subjects functional statusHR variability changes during sleep reflect the restoration of autonomic HR control and correlate to the HR responses to orthostatic testHR variability analysis during sleep is a valuable tool for clinical diagnostics

Psychoemotional status (Depression, Anxiety)Sleep disturbances (Insomnia)Cardiovascular pathologySleep more active than passive processDepression & Coronary artery diseaseAfter 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)

Depression & Coronary artery disease (Lambert 2002): Altered platelet function Reduced parasympathetic controlIncreased catecholamine levelsAbnormal inflammatory responsesAbnormalities in thyroidal functionIncreased risk factors (smoking, overweight, less prone to exercise, to enroll rehabilitation programme etc)Cardiotoxicity of antidepressants (TCAs)Psychoemotional status (Depression, Anxiety)Sleep disturbances (Insomnia)Cardiovascular pathologySleep 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 complications270with Hypertension304with Congestive Heart failure648with CAD and Diabetes113

CAD PatientsNYHA ClassCAD Patients7.2*I2.81.36.9*I52.89.612.913.689.086.4319.7II9.0*I; II5.9*7.8*5.2PSQI2.72.42.72.4BM, %1.1*I2.11.3*6.1S4, %6.8*I9.67.0*11.0 S3, % 53.952.353.150.6S2, %9.49.49.57.8S1, %10.9*I; II13.312.212.9REM Sleep, %15.1*I; II10.914.1*9.3WASO, %97.7101.992.9*120.6REM lat., min.84.9*I, II89.185.9*90.7SE, %313.8*I335.8318.1*352.2TST, min.IIIIHealthy SubjectsSleep quality in healthy subjects and CAD patients* p< .05

5.22.46.111.050.67.812.99.3120.690.7352.2Healthy SubjectsDiabetesCAD PatientsHeart failureHyper- tensionCAD8.8*3.1

0.65*4.9*55.4*10.411.813.7*88.1*86.3*308.0*41:3, 1:4, 2:3, 2:48.2*6.57.1*PSQI3:42.62.92.8BM, %1:3, 1:41.1*1.7*1.9*S4, %1:4, 3:46.7*7.4*7.9* S3, % 52.851.652.5S2, %1:2, 2:39.111.0*8.5S1, %1:3, 1:4, 2:311.813.413.8REM Sleep, %1:3, 2:315.9*12.012.6WASO, %90.888.1*92.5*REM lat., min.1:3, 2:384.1*88.087.4SME, %317.1*326.6*326.4*TST, min.p321Sleep quality in healthy subjects and CAD patients distributed according prevalence of hypertension, heart failure and diabetes

0.01

12.3

14

57

15

7

0.45

1.59

30

30

33

0.11

4.51

51

40

2With Anxiety and Depression

0.33

3.41

4

18

6

8

0.36

2.05

10

14

12

0.99

0.01

17

16

3With Depression

0.09

6.53

6

55

18

8

0.17

3.59

18

34

35

0.99

0.03

40

40

7With Anxiety

8

47

33

16

30

28

46

48

47

9

Without Anxiety and Depression3210210IIIIII

Angina Pectoris (Class)pHeart Failurep

NYHA Functional ClassRandomization of 970 CAD pts according clinical statusp

With Anxiety and DepressionCAD PatientsWith DepressionWith AnxietyWithout Anxiety and Depression10.72.81.25.954.210.110.615.395.384.7316.541:2, 1:4, 2:3, 2:4, 3:47.48.76.5PSQI3.12.62.7BM, %1:3, 2:40.51.41.4S4, %1:3, 1:4, 2:3, 2:44.57.47.3 S3, % 55.353.052.9S2, %11.29.69.3S1, %1:3, 1:4, 2:310.312.412.8REM Sleep, %15.113.513.6WASO, %95.090.292.9REM lat., min.84.986.586.4SE, %1:3304.3314.6322.5TST, min.p321Sleep quality in CAD patients groups distributed according Anxiety and Depression

* - p

Maximal HR response to active orthostasis at evening and morning-time in CAD patients

Poincare plots of RR interval for the CAD patients with depressionwithout depression

HR variability analysis Healthy SsCAD pts

Personality traits and heart rate variability predict long-term cardiac mortality after myocardial infarctionClara Carpeggiani, Michele Emdin, Franco Bonaguidi, Patrizia Landi, Claudio Michelassi, Maria Giovanna Triv ella, Alberto Macerata, and Antonio LAbbateEuropean Heart Journal, 2005, 26, 16, 1612-1617.

Personality traits and heart rate variability predict long-term cardiac mortality after myocardial infarctionClara Carpeggiani, Michele Emdin, Franco Bonaguidi, Patrizia Landi, Claudio Michelassi, Maria Giovanna Triv ella, Alberto Macerata, and Antonio LAbbateEuropean Heart Journal, 2005, 26, 16, 1612-1617.Table 4. Univariate and multivariable Cox proportional regression analyses

HR (95% CI)P-valuePredictors of deathUnivariate model 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 - I

Restoration of cardiovascular function during sleep was related not only to TST, however with SWSDecreased sympathetic input was observed in pts with anxiety, while reduction of both, sympathetic and parasympathetic one, was characteristic to pts with depressionCAD pts with depression and anxiety might be seen as loosing ability for restoration of autonomic control of HRConcluding - II

Biofeedback & TreatmentCoronary artery diseaseInsomniaDepression & Anxiety?

Biofeedback: Main mechanismAn 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) 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 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 baroreflexBiofeedback exercises the baroreflexes, and renders them more efficient

Heart Rate Reactions at Paced BreathingVaschillo 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 BFBLehrer P. M. et al. Heart Rate Variability Biofeedback Increases Baroreflex Gain and Peak Expiratory Flow. Psychosomatic Medicine 65:796-805 (2003).

Biofeedback and HR VariabilityLehrer 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.Resonant frequency HR variability biofeedback increases baroreflex gain and peak expiratory flow in healthy individuals and has positive effects in treatment of asthma patients

Biofeedback and Baroreflex5 healthy Ss learned to control oscillations in HR using biofeedback training to modify their HR variability at 7 frequencies within the range of 0.010.14 HzThe highest oscillation amplitudes were produced in the range of 0.0550.11 Hz for HR and 0.020.055 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 systemThe 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 disordersVaschillo 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 Sensitivity32 students, 3 biofeedback sessions with four 5-min trials each, in which they had to increase and decrease baroreflexBRS 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 periodThe opposite changes were observed during the Decrease ConditionThe study demonstrates the modification of the baroreflex function through biofeedbackReyes del Paso G. A. & Gonzlez 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 pressure12 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 2-week 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 levelsRau 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 Variability20 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 VariabilityThese results tentatively suggest that the biofeedback method can produce long-term 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: 5864.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 skillsetc

Heart Rate VariabilityPowerful diagnostic tool

Useful for treatment of various disorders using biofeedback

AcknowledgementProf. Danguol emaityt, M.D., D. Sci. (habil.)Audrius Alonderis, M.D.Julija Broaitien, M.D., D. Sci. (habil.)Inga Duonelien, M.D., D. Sci.Andrejus EIdukaitis, D. Sci.Vaidut Gelinien, M.D., D. Sci.Ramut Ksait, D. SciArvydas Martinknas, D. Sci.Aurelija Podlipskyt, Dip. Eng., D. Sci.Graina Valyt, M.D.Linas Zakarevicius, Dip. Eng.Geriuldas iliukas, M.D., D. Sci. (habil.)

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