Download Heart-rate Variability Christoph Guger, 10.02.2004

Heart-rate Variability
Christoph Guger, 10.02.2004
Heart-rate Variability (HRV)
1965 Hon & Lee – Fetal distress alterations in interbeat
intervals before heart rate (HR) changed
1980 HRV is strong and independent predictor for mortality
following myocardial infarct
Signs of increased sympathetic and reduced vagal activity has
triggered the development of quantitiative markers
Content:
Standardization and definition of terms
Standard methods for measurement
Physiological and pathophysiological correlation
HRV in high-altitude medicine
QRS Complex Detection
Continuous ECG - > find the QRS complexes in the time series.
This signal is non-uniformly sampled and can be shown as
Tachogram (the RR intervals are plotted over the number of RR
intervals).
HRV Time Domain Measures
In a continuous ECG recording the normal-to-normal (NN or RR)
intervals can be determined (interval from one QRS complex to the
next).
• Simple Time Domain Measures
• Complex Time Domain Measures
a) derived from direct measurement of NN intervals
b) derived from difference between adjacent NN intervals
This measure can be used to investigate variations of the
heart rate secondary to tilt, Vasalva manoeuvre, to
describe the difference between night and day,…
Simple Time Domain Measures
Mean RR interval: e.g. 1000 ms or 256 samples
Mean heart rate: e.g. 60000 ms / 1000 ms = 60 bpm
Minimum RR interval: e.g. 700 ms
Maximum RR interval: e.g. 1200 ms
Difference between RRmin and RRmax: 500ms
Difference between night and day,...
From entire recording or from smaller segments ->
allows comparision e.g. rest, sleep,...
Complex Time Domain Measures
SDNN ... Standard deviation of RR intervals
SDNN = SQRT ( VAR(RR) )
Reflects the cyclic components responsible for variability in the
period of recording
Normally over 24 hours -> describes short term high frequency
variations and low frequency components !
Recording Period ↓ -> SDNN estimates shorter -> shorter cycles
Total variance increases with recording length -> SDNN depends
on recording length
Inappropriate to compare SDNN measures of
different recording length
Standardize recording length: 5 min or 24 h
Segmented Measures
SDANN … standard deviation of the average NN interval
calculated over short periods e.g. 5 minutes
µ1
µ2
µ3
SDANN = STD ( [µ1,
µ4
...
µN
µ2, µ3, µ4,... µN])
Estimates changes in heart rate due to cycles
longer than 5 minutes (for 5 min intervals)
SDNNindex … mean of 5 min standard deviation of NN intervals
calculated over 24 h
STD1
STD2
STD3
STD4
...
STDN
SDNNindex = MEAN ( [STD1, STD2, STD3, STD4,... STDN])
Measures variability due to cycles shorter than 5
min (for 5 min intervals)
Interval Differences
RMSSD … square root of the mean squared difference of
successive NN intervals
D1
D2
∆ D1=|D2-D1|
D3
D4
...
∆ D2=|D3-D2| ... ∆ DN
RMSSD = SQRT ( MEAN ([∆ D12, ∆ D22,... ∆ DN2]) )
DN
Interval Differences
NN50 … number of intervals of successive NN intervals greater
than 50 ms
Segments must be equal !!
D1
D2
∆ D1=|D2-D1|
D3
D4
...
∆ D2=|D3-D2| ... ∆ DN
NN50 = NN50+1 if ∆ Di > 50 ms
pNN50 ... NN50 / (total number of NN intervals)
DN
Geometric Measures
Histogram of RR intervals
a)
Basic measurements of the geometric pattern e.g width of the
histogram
b)
Geometric pattern is interpolated by mathematically defined
shape (e.g. exponential curve)
Most experience with bins of 8 ms (128 Hz)
Geometric Measures
HRVtriangular index … integral of density distribution / maximum of
density distribution
maximum number
HRVtriangular index = Number of all NN intervals / maximum
number
Dependent on the length of the bin -> quote the bin size
+ relative insensitive to the analytic quality of the series of NN
intervals
- need of reasonable number of NN intervals to generate the
geometric pattern (in practice 20 min to 24 h)
- not appropriate to assess short-term changes in HRV
Summary and Recommendation
Many measures correlate closely -> following measures are
recommended
SDNN … estimate of overall HRV
HRVtriangular index … estimate of overall HRV
Easy pre-processing
SDANN … estimate of long term components
RMSSD … estimate of short term components
• Methods for overall HRV, short and long-term can NOT replace
each other
• Method should correspond to aim of study
• Do NOT compare overall measures from recordings with different
duration
Tilt Table Experiment: HRV Time Domain Measures
Rest
Tilt
Tilt Table Experiment: Histogram
4th Presencia meeting - TUG
HRV Frequency Domain Measures
Spectral analysis provides information how power is distributed as
function of frequency (Power Spectral Density – PSD) -> detects
periodic oscillations
Non-parametric methods
Fast Fourier Transformation (FFT)
+ simple and fast
Parametric methods
+ smoother spectral components
+ easy post-processing of the spectrum
+ easy identification of the central frequency
+ accurate estimate of PSD even for small number of samples
- order of model must be chosen
HRV Frequency Domain Measures
Absolute Measures
ULF – ultra low frequency band <0.003 Hz
VLF – very low frequency band 0.003 – 0.04 Hz
LF – low frequency band 0.04 – 0.15 Hz
HF – high frequency band 0.15 – 0.4 Hz
The distribution of LF and HF is not fixed and varies with autonomic
modulation of the heart rate
The energy in HF is vagal mediated
The energy in LF and VLF are due to both sympathetic and
parasympathetic systems
Relative Measures
The normalization minimizes the effect of changes of Total Power
(TP) on LF and HF
Lfnorm = LF / (TP-VLF), unit n.u.
Hfnorm = HF/(TP-VLF), unit n.u.
LF/HF
HRV Frequency Domain Measures
Shows balanced behavior of the 2 branches of the autonomic
nervous system
Normalized units should be quoted with absolute values to describe
the distribution of spectral components
Short term recording (5 minutes)
VLF is not reliable
Long term recording (24 hours)
Results include also ULF
Problem that heart period modulation responsible for LF and HF is
not stationare during 24 h
Tilt Table Experiment: HRV Frequency Domain
Measures
Rest
Tilt
Tilt Table Experiment: HRV Map
Technical Requirements
Deviation from following requirements may lead to
unreproduceable results that are difficult to interpret
Sampling rate
Low sampling rate produces jitter in R wave point -> alters
spectrum
Optimal: 256 – 500 Hz or higher
If sampling rate is lower (in any case >100 Hz) -> use interpolation
algorithm to refine R wave point
QRS point must be accurate
Ectopic beats, arrhythmic events, missing data and noise effects
the estimation of PSD
-> use linear interpolation to reduce error
-> use error free data
Technical Requirements
Filter
Filter much lower than 200 Hz creates jitter in QRS point -> error in
RR interval
Editing
Manuel editing of QRS complexes must be very accurate
Automatic filters can NOT replace manuell editing (remove
intervals which differ more than 20 % from previous interval)
Algorithm Standards
QRS detection produces event series
Ri – Ri-1 versus time
-> irregularly time sampled signal: Tachogram
Spectrum is calculated of Tachogram or of interpolated DES
Recommendation: Tachogram + parametric PSD estimation
DES + non-parametric PSD estimation
or
DES + parametric PSD estimation
Sampling frequency of DES must be high enough that Nyquist
frequency is not in frequency range of interest (2 Hz or 4 Hz)
Windowing function: Hanning, Hamming or triangular
Order of parametric method: 8-20
Correlation between Time and Frequency Domain
Time Domain
Frequency Domain
SDNN
TP
HRVtriangular index
TP
SDANN
ULF
SDNNindex
Mean of 5 min TP
RMSSD
HF
SDSD
HF
NN50
HF
pNN50
HF
Stability of HRV
Many studies show that short-term measures return to baseline after
mild exercise,...
More powerful stimuli, e.g. maximum exercise result in longer effect
Duration of ECG recording
Duration is dictated by nature of investigation
-> standardization is needed
FDM – for short-term recording
At least 10 times the wavelength of the LF band
1 min is needed for HF
2 min are needed for LF
5 min should be used for standardization
Use Compressed Spectral Array to show variations over long time
SDNN, RMSSD can be used for short term, but FD are more easily
to interpret
TDM are ideal for long-term
FDM are difficult to interpret for long-term
Physiological correlates of HRV
HR and HRV are largely under control of autonomic nervous
system
Parasymphathetic influence on HR is mediated by vagus nerve
(release of acetylcholine -> slow diastolic depolarization)
Sympathetic influence on HR is mediated by epinephrine and
norepinephrine -> acceleration of the diastolic depolarization
Under rest:
vagal tone is prominent
variations are largely dependent on vagal
modulation
vagal and sympathetic activity interact
vagal impulses are brief (acetylcholine is rapidly
hydrolyzed)
Vagal activity inhibits sympathetic activity and vice versa
Physiological correlates of HRV
HRV is modulated by central (vasomotor and respiratory centres)
and peripheral (oscillations in arterial pressure and respiratory
movements) oscillations
HF ... Vagal activity
LF ... Marker for sympathetic modulation (when expressed in n.u.)
and marker for sympathetic and parasympathetic modulation
Discrepance: under exercise (SA ↑ ) LF is reduced !
Explanation:
during SA ↑ -> Tachycardia -> TP ↓
during PA ↑ -> TP ↑
Therefore, use normalized units
SA ↑ -> TP ↓ : Lfnorm= LF/(TP↓ ) -> Lfnorm ↑
PA ↑ : Lfnorm=LF/(TP↑) -> Lfnorm ↓
Tilt Table Experiment: HRV Frequency Domain
Measures
Rest
Tilt
Lfnorm ↑ : tilt, standing, mental stress, moderate exercise, physical
activity
Hfnorm ↑ : controlled respiration, cold stimulus in face, ...
LF/HF show balance of SA and PA
HRV changes related to pathology
Myocardial infarction
HRV ↓ after MI -> PA ↓ , SA ↑
Heart transplantation
HRV ↓ ↓ , no definite spectral components
Myocardial dysfunction
HRV ↓
Tetraplegic patients
Patients with complete high spinal cord lesion have intact efferent
vagal and sympathetic neural pathways to sinus node.
But spinal sympathetic neurons have no control of baroreflex
inhibitory impuls
LF ↓ ↓
Tests of g.MOBIlab on Dachstein
What
4th Presencia meeting - TUG
Effects of a fast ascent with the cable car on EEG and
ECG
Measurements in the Dachstein (3000 m) region
Altitude difference of 998 m (1702 – 2700m)
Ascent with the cable car in 6 min
Drop of oxygen partial pressure has effect on human body
Measurements
• 13 people performed a reaction time task
• paradigm
• simultaneous EEG and ECG recording
• 3 time points:
• in the morning in the hotel (initial state)
• during the cable car ascent
• at the mountain
• each measurement lasted 6 minutes
g.MOBIlab
Mobile Pocket PC biosignal acquisition system
1 ECG derivation (Einthoven I)
2 EEG derivations (right and left hemisphere)
Button to log human responses
Content
What
4th Presencia meeting - TUG
Measurement in the Cable Car
Biosignal Visualization
Results
1700 m
2700 m
Increase of the heart rate from 85 to 98
beats per minute during the cable car
ride
ERD/ERS-Analysis:
red colors - amplitude attenuation
blue colors – amplitude enhancement
Results
Parameter
Valley
Mountain
Heart rate [bpm]
69,72
82,47
SDNN [ms]
53,12
28,80
HRV-Index [nr]
12,99
8,04
PNN50 [%]
12,86
1,44
LF/HF [1]
1,96
3,09
Reaction time [ms]
333,44
367,28
Alpha ERD [%]
23,72
28,48
Beta ERD [%]
11,29
21,65
Guger Technologies OEG
Herbersteinstrasse 60
8020 Graz, Austria
Phone: +43-316-675106
Fax: ++43-316-675106-39
Email: [email protected]
Web: www.gtec.at
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