179 lines
6.8 KiB
HTML
179 lines
6.8 KiB
HTML
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HREF="index.shtml">A dynamical model for</A>
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<H1><A NAME="SECTION00060000000000000000"></A>
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<A NAME="s:conclusions"></A>
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<BR>
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Conclusions
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</H1>
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A new dynamical model has been introduced which is capable of replicating
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many of the important features of the human ECG.
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Moreover, many of the morphological changes observed in the human ECG
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manifest as a consequence of the geometrical structure of the model.
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Model parameters may be
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chosen to generate different morphologies for the PQRST-complex.
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The power spectrum of the RR-intervals can be selected <I>a priori</I> and used
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to drive the ECG generator. This allows the operator to prescribe specific
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characteristics of the heart rate dynamics such as the mean and standard
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deviation of the heart rate and
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spectral properties such as the LF/HF ratio.
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In addition the average morphology can be controlled by specifying the
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positions of the P,Q,R,S and T events and the magnitude of their effect
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on the ECG.
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Having access to a realistic ECG provides a benchmark for testing
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numerous biomedical signal processing techniques. In order to establish
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the operational properties of these techniques in a clinical setting,
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it is important to know
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how they perform for different noise levels and sampling frequencies.
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A number of applications and simple extensions of the model are possible:
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(i) By fitting (see [<A
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HREF="node8.html#mcsharry99a">17</A>]) the model
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to the morphology of a particular subject's ECG
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and the power spectrum of their RR-intervals,
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a database of realistic ECGs could be created.
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This database could be employed for statistical hypothesis testing.
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Furthermore, it may be possible derive a corrected QT-interval which is
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independent of the heart rate.
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(ii) The synthetic ECG could be used to assess the effectiveness of
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different techniques for noise and artefact removal.
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These could be evaluated by adding noise and/or artefact onto the
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synthetic signal and then comparing the original with the processed signal.
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(iii) Abnormal morphological changes with time could be introduced
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by using a parameter to control the position of any of the P,Q,R,S or T events.
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This extension would be particularly useful for testing techniques
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which aim to detect ST depression or elevation by decreasing or
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increasing the <IMG
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WIDTH="12" HEIGHT="14" ALIGN="BOTTOM" BORDER="0"
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SRC="img12.png"
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ALT="$z$">-position of the T wave over time.
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Similarly QT prolongation could be replicated by moving the T point away
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from the Q point in the <IMG
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WIDTH="41" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
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SRC="img11.png"
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ALT="$(x,y)$"> plane (increasing
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<!-- MATH
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$\theta_T - \theta_Q$
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-->
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<IMG
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WIDTH="59" HEIGHT="29" ALIGN="MIDDLE" BORDER="0"
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SRC="img69.png"
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ALT="$\theta_T - \theta_Q$">).
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(iv) The model could be used to produce multi-lead ECG signals by
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introducing a measurement function which maps from the
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<IMG
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WIDTH="56" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
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SRC="img1.png"
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ALT="$(x,y,z)$"> model space to the ECG signal: <IMG
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WIDTH="94" HEIGHT="31" ALIGN="MIDDLE" BORDER="0"
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SRC="img70.png"
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ALT="$s = h(x,y,z)$">. Different lead
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configurations and modulations due to respiration and movement of the
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cardiac axis could be modelled using time-dependent functions for <IMG
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WIDTH="13" HEIGHT="15" ALIGN="BOTTOM" BORDER="0"
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SRC="img71.png"
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ALT="$h$">.
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(v) Abnormal beats, such as ectopics, can be simulated by
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modifying the position of the R-peak for one cycle of the dynamics.
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The new model presented here reflects a data-driven approach to modelling the
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electrical activity of the heart. Key physiological features have been
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incorporated using motion of a trajectory throughout a three-dimensional state
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space. The quasi-periodicity of the cardiac cycle is represented by
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attraction towards a limit cycle.
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The model produces QT-intervals and R-peak height variation (RSA)
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which vary linearly with the RR-intervals
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as has been found in real ECGs [<A
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HREF="node8.html#moody86">6</A>,<A
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HREF="node8.html#davey99">4</A>].
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It is hoped that this model will provide a valuable tool
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for testing biomedical signal processing algorithms applied to ECG signals
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with different sampling frequencies and
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levels of noise and/or movement artefact.
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HREF="index.shtml">A dynamical model for</A>
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HREF="node5.html">Results</A>
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<ADDRESS>
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2003-10-08
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</ADDRESS>
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