170 lines
6.9 KiB
HTML
170 lines
6.9 KiB
HTML
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<HTML>
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<TITLE>Introduction</TITLE>
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HREF="index.shtml">A dynamical model for</A>
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<H1><A NAME="SECTION00010000000000000000">
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Introduction</A>
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</H1>
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The
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electrocardiogram (ECG) is a time-varying signal reflecting the
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ionic current flow which causes the cardiac fibres to contract and
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subsequently relax.
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The surface ECG is obtained by recording the potential difference between
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two electrodes placed on the surface of the skin.
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A single normal cycle of the ECG represents the successive atrial
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depolarisation/repolarisation and ventricular depolarisation/repolarisation
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which occurs with every heart beat. These can be approximately associated
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with the peaks and troughs of the ECG waveform labelled P,Q,R,S and T as
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shown in Fig. <A HREF="node1.html#f:garipqrst">1</A>.
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Extracting useful clinical information from the real (noisy) ECG requires
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reliable signal processing techniques [<A
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HREF="node8.html#goldberger77">1</A>].
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These include R-peak detection [<A
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HREF="node8.html#pan85">2</A>,<A
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HREF="node8.html#kaplan91">3</A>],
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QT-interval detection [<A
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HREF="node8.html#davey99">4</A>]
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and the derivation of heart rate and respiration rate
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from the ECG [<A
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HREF="node8.html#moody85">5</A>,<A
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HREF="node8.html#moody86">6</A>].
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The RR-interval is the time between
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successive R-peaks, the inverse of this time interval gives the
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instantaneous heart rate.
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A series of RR-intervals is known as a RR tachogram and variability of
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these RR-intervals reveals important information about the physiological state
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of the subject [<A
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HREF="node8.html#malik95">7</A>].
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At present, new biomedical signal processing algorithms are usually evaluated
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by applying them to ECGs in a large database such as the Physionet
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database [<A
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HREF="node8.html#physionet">8</A>].
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While this gives the operator an indication of the accuracy of a given
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algorithm when applied to real data, it is difficult to infer how the
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performance would vary in different clinical settings with a range of
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noise levels and sampling frequencies.
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Having access to realistic artificial ECG signals may
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facilitate this evaluation.
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<DIV ALIGN="CENTER"><A NAME="f:garipqrst"></A><A NAME="53"></A>
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<TABLE>
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<CAPTION ALIGN="BOTTOM"><STRONG>Figure 1:</STRONG>
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Morphology of a mean PQRST-complex of an ECG recorded
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from a normal human.</CAPTION>
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<TR><TD><IMG
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WIDTH="352" HEIGHT="279" BORDER="0"
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ALT="\begin{figure}
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\centerline{\psfig{file=garipqrst.eps,width=7.75cm}}
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\end{figure}"></TD></TR>
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</TABLE>
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</DIV>
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This paper presents a model for generating a synthetic ECG signal with
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realistic PQRST morphology and prescribed heart rate dynamics.
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The aim of this model is to provide a
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standard realistic ECG signal with known characteristics,
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which can be generated with specific statistics
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such as the mean and standard deviation of the heart rate
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and frequency-domain characteristics of heart rate variability (HRV),
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such as the LF/HF ratio, defined as the ratio of power between 0.015 and
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0.15 Hz and 0.15 and 0.4 Hz in the RR tachogram [<A
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HREF="node8.html#malik95">7</A>].
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By generating a signal which represents a <I>typical</I> human ECG, this
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facilitates a comparison of different signal processing techniques.
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A synthetic ECG can be generated with different sampling frequencies and
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different noise levels in order to establish the performance of
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a given technique. This performance can be presented, for example,
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as the number of true positives, false positives, true negatives and false
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negatives for each test. Such performance assessment could be used as
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a ``standard'' and would enable clinicians to ascertain which biomedical
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signal processing techniques were best for a given application.
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The layout of this paper is as follows; section <A HREF="node2.html#s:morphology">II</A>
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summarises the physiological mechanisms underlying the cardiac cycle and
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reviews the morphological variability which is reflected in the ECG signal.
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A brief review of HRV is presented in
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section <A HREF="node3.html#s:hrv">III</A>. The dynamical model is introduced in
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section <A HREF="node4.html#s:model">IV</A> and investigated in section <A HREF="node5.html#s:results">V</A>.
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Section <A HREF="node6.html#s:conclusions">VI</A> concludes and discusses extensions to
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the model which may be useful for simulating specific disorders.
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<ADDRESS>
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2003-10-08
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