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Putting your Heart into Physics

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Axel Urhausen (Medical Doctor, Professor, Physician for. Olympic Rowing Team) ... Wilfred Kindermann (Medical Doctor, Head of Institute, ... – PowerPoint PPT presentation

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Title: Putting your Heart into Physics


1
Putting your Heartinto Physics
  • Jonas Sperber (Medical Student)
  • Axel Urhausen (Medical Doctor, Professor,
    Physician for
  • Olympic Rowing Team)
  • Peter Siegel (Physicist)
  • Wilfred Kindermann (Medical Doctor, Head of
    Institute,
  • Professor, Former European
    Champion, Physician
  • for German National Soccer
    Team, Olympic Team,
  • etc. )
  • Institut fuer Sport- und Praeventivmedizin
  • Universitaet des Saarlandes

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Outline
  • 1. Measurement of RR intervals
  • 2. Some Physiology (three time scales)
  • Short Time Scale (3-4 seconds)
  • Response function of the heart (short time
    scale)
  • a) to sinusoidal input
  • b) Square wave input
  • Middle Time Scale (10-30 seconds)
  • a) Fourier Analysis
  • Long Time Scale
  • a) Methods from non-linear dynamics
    (long time scale)
  • 6. Dynamics of heart rate and its variability
    (a new analysis method)

7
RR Interval The time between successive R
peaks in an EKGData a series of
times (in milliseconds) t1, t2, t3,
.Analysis Mathematics of Time Series Analysis
(TSA)
8
Measurement Equipment
  • Cardia (TF4)- Monitor belt telemetry to PC
    15,000
  • Polar Heart Rate Monitor S810 Monitor belt
    telemetry to watch for download 600
  • Pickup loop with HC11 microprocessor, download to
    PC 120
  • Pickup loop with comparator chip to PC 20

9
Example of RR Interval Variation
10
Resting Heart Rate
  • Heart Rate at rest is regulated by the Autonomous
    Nervous System
  • Average Resting rate (B) determined from 3
    parameters
  • B0 Basic Rate
  • n Para-Sympathetic activity factor
    (1 P)
  • m Sympathetic activity factor (1
    S)

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Dynamics of RR Interval Variations
  • Short
    time scale Medium time scale

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Example of Lying vs. Standing HRVBreathing
oscillations vs. Meyer WavesShort time scale
vs. Long time scale
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Respiratory Sinus ArrythmiaLying positionShort
time scale
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Blood Pressure-Heart Rate Control
InteractionStanding PositionLong time scale
(Meyer Waves)
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Response Function of the Human HeartShort Time
Scale
  • Signal in
    Signal out
  • ? ?
  • Signal in Breathing Function
  • Signal out RR Interval time series
  • Periodic Inputs result in Periodic Outputs of the
    same period.
  • Examples Sinusoidal, Square Wave

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Sinusoidal Breathing Input
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Respiratory Sinus Arrythmia as a function of
breathing frequencyResponse Spectrum
Solid line lying Dashed line -
standing
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Response Spectrum
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Amplitude of response is proportional tothe
Amplitude of the input
21
Response to Step Function BreathingInhale
(breath hold for 10 heartbeats)Exhale (breath
hold for 10 heartbeats)
Non-linear response response to step up not
equal to minus the response to step down.
22
  • Are there any stochastic aspects in beat to beat
    heart rate variations?

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Summary of Short time-scale dynamics
  • Most important influence is breathing
    Respiratory Sinus Arrhythmia
  • The response spectrum (sinusoidal breathing) has
    the characteristics of a resonance. The
    resonance frequency is between 0.05 and 0.1 Hz
    (10-20 heartbeats).
  • The response spectrum depends on posture (lying
    vs standing)
  • Changes affected by para-sympathetic nerve
    activity

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Analysis of Medium Time scale changes(10 30
heartbeats)
  • Fourier Spectrum

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Scaling Analysis of Long Time scale dynamics
1. Choose a quantity F that is a measure
of heart rate variability or fluctuation on a
time scale of n heartbeats. Examine the
scaling properties of this quantity
a) F Fourier Amplitude b) F
Detrended Fluctuations c) F
Wavelet Coefficients d) etc
27
Plot of Fourier Amplitude vs. Period
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Fixed Counting time Measurements
  • Find average number of heartbeats and its
    standard deviation for a fixed counting time
  • Note this is similar to the statistics of
    nuclear counting experiment where

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Dynamics of Heart Rate and its Variability
  • Measure Pulse (B) and a variability parameter (V)
    as the system undergoes quasi-static change
  • Since both variables depend on the
    Para-sympathetic activity, P, plot B vs. V . One
    can learn about the para-sympathetic activity.
  • Assumptions
  • a)
  • b)

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Since Plt0.5, the series converges rapidly. Also,
V goes to zero as P goes to zero, so
  • We choose V Respiratory Sinus Arrythmia at a
    breathing rate of 12 breaths/min.

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Equation of State
  • By plotting the pulse B vs. the variability V,
    one obtains information about the type of process
    that is causing the change in heart rate!
  • If m is constant during the process, one can
    determine mB0 and P from the graph!!!

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