Transcript Document

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
AHI5
n=59
2
3-4
5>AHI15
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