Arrhythmia analysis (heart rate variability)

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Arrhythmia analysis(heart rate variability)

• Johanna Uusvuori
• 25.11.2004

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Contents

• 1. Introduction one slide of autonomic nervous
  system
• 2. Why does heart rate vary?
• 3. Analysis methods
• a) Time domain measures
• b) Model of the heart rate
• c) Representations of heart rate
• d) Spectral methods (introduction)
• 4. Summary

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Human nervous system
Somatic Autonomic
Autonomic nervous system regulates individual
organ function and homeostasis, and for the most
part is not subject to voluntary control
Somatic nervous system controls organs under
voluntary control (mainly muscles)

• Parasympathetic
• rest

Sympathetic Fight, fright, flight
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Why does heart rate vary?Why is the variation
interesting?

• Autonomic nervous system regulates the sinus
  node
• Analysis of the sinus rhythm provides
  information about the state of the autonomic
  nervous system

• Heart rhythm is due to the pacemaker cells in
  the sinus node

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Starting point of the analysis of the heart rate
variability

• sinus node ? P-wave (hard to detect)
• analysis methods are based on measuring
  RR-intervals (RR-interval can be used instead of
  PP-interval, since PR-interval constant )
• NN-intervals RR-intervals but non-normal
  intervals excluded

RR-interval
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Problems in the analysis

• In laboratory analysis is easy.
• 24 h measurement (Holter)
• ? problems wrong corrects,
• undetected beats,
• 100 000 RR-intervals
• Analysis methods are sensitive to errors (time
  domain methods less sensitive, spectral most
  sensitive)

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Time domain measures of HR

• Long term variations in heart rate
• (due to parasympathetic activity)
• are described by
• - SDNN standard deviation of NN-intervals (1
  value/ 24 h)
• - SDANN standard deviation of NN-intervals in
  5-minute segments
• (288 values / 24 h)
• Short term variations in heart rate
• (due to sympathetic activity)
• - rMSSD standard deviation of
• successive interval differences
• - pNN50 the proportion of intervals
• differing more than 50 from the previous
• interval (used clinically)

Successive interval differences
mean int.diff.
Intervals
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Time domain measures of HR

• Histogram approach
• has been used to study arrhyhtmias (in addition
  to spontane variations in HR)
• possible to remove artefacts and ectopic beats
• only for 24 h measurement
• width of the peak determines the variation in the
  heart rate

Peak of short intervals due to falsely detected
T-waves
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Model of the heart rateIntegral pulse frequency
modulation (IPFM) model

• Main idea
• We have the output event series
• We search for input m(t) that modulates the HR
  (autonomic nervous system)
• m0 is the mean heart rate

input m(t)
INTEGRATOR
output
THRESHOLD
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IPFM-model

• Bridge to physiology pacemaker cells collect the
  charge until threshold. Then action potential if
  fired.
• When this equation is valid, produce a peak to
  the event series

m0 mean heart rate tk time of
QRS-complex m(t) modulation of heart rate R
threshold
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Representations of the heart rate

• Quantities to describe the heart rate
• Lengths of the RR-intervals
• Occurence times of the QRS-complexes
• Deviations of the QRS-complex times from the
  times predicted by a model
• With IPFM-model we can test which method is best
  in finding the modulation m(t).

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Representations of the HR 1. RR-interval series

• Interval tachogram inverse
• These are functions of k ( of heart beats). If
  they can be changed to functions of time, several
  methods from other fields can be used in the
  analysis.
• Interval function inverse (uunevenly
  sampled)
• Interpolated interval fuction inverse
• (evenly sampled, function of t)
• - sample and hold interpolation (and better
  methods)
• - sample hold produces high frequency noise
• low pass filter ? before resampling

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Representations of the HR 2. Event series

• Event series QRS occurence times
• In low frequencies info of HR, in high
  frequencies noise ? new representation low-pass
  filter h
• h sin(2piFct)/t for example. After some limit
  the terms in the sum are allmost zero.
• If in the IPFM-model m(t)sin(F1t), a proper
  low-pass filter removes other stuff
• except the m(t)
• ? estimate for m(t)dLE(t)

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Representations of the HR 3. Heart timing

• Unlike previous representations, this is based on
  the IPFM-model.
• The aim is to find modulation m(t).
• Heart timing representation
• k of heart beat T0 average
  RR-interval length
• dHT is the deviation of the event time tk from
  the expected time of occurence. The expected time
  of occurence is kT0.
• By calculating Fourier transform of the dHT and
  m(t), one can see that the spectrum of dHT and
  m(t) are related, and spectrum of m(t) can be
  calculated from the spectrum of dHT.

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Representation of the HRPerformance of the
representations

• Best method to predict m(t) of IPFM-model is to
  use heart timing representation (which is based
  on this model)
• However heart timing representation does not
  fully explain the heart rate variability of
  humans
• ? the IPFM-model might not be accurate

The End of the representation-part
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Spectral methodsWhich kind of information is
gained?
New topic what kind of modulating signals do we
have?

• Oscillation in heart rate is related to for
  example
• body temperature changes 0.05 Hz (once in 20
  seconds)
• blood pressure changes 0.1 Hz
• respiration 0.2-0.4 Hz
• Power of spectral peaks ? information
• about pathologies in different
• autonomic funtions

Power spectrum of a heart rate signal during rest
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Spectral methodsWhich kind of information is
gained?

• Peaks of thermal and blood pressure regulation
  sometimes hard to detect ?
• frequency ranges used 0.04-0.15 Hz and
  0.15-0.40 Hz
• Sympathicus increase, low-frequency power
  increase
• Parasympathicus increase, high-frequency power
  increase
• Ratio between two spectral power describes
  autonomic balance

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Spectral methodsProblems of spectral analysis

• Stationarity important
• Extrabeats violate the stationarity, but they can
  be removed in the analysis
• Undetected beats are a bigger problem
• ? spectral analysis can not be conducted, if
  they are present
• HR determines the highest frequency that can be
  analyzed 0.5mean hr

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Summary

• Autonomic nervous system ? heart rate varies
• Measurment of HR ? info about autonomic system
• Analysis methods of HR
• Time domain methods ? standard deviations
• Representations of the heart rate
• (intervals, times, heart timingmodel based)
• Model that can predict heart rate IPFM-model
• Spectral analysis (to be continued in the next
  talk)

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