Signal Averaged ECG

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Signal Averaged ECG
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High Resolution Electrocardiography

• A high-resolution electrocardiogram detects very
  low amplitude signals in the ventricles called
  'Late Potentials' in patients with abnormal heart
  conditions.
• A standard electrocardiogram cannot detect these
  signals.
• The presence of late potentials is widely
  accepted to have prognostic significance in
  patients after AMI

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SAECG

• The ECG is a graphical representation of the
  electrical potentials generated by the heart
• Based on the resolution of the digital recording
  of analog ECG signals, the instruments
  techniques may be categorized into 2 types ? 1)
  Low-resolution (or standard) ECG, and ? 2)
  High-Resolution ECG (HRECG)
• A standard 12-Lead ECG is a typical example of a
  widely used low-resolution instrument that
  records 9 sec of cardiac data
• A SAECG is a typical example of a High-Resolution
  ECG
• SAECG records ventricular ECG signals of very low
  magnitudes called 'Ventricular Late Potentials'
  (VLP) by averaging a number of signals (QRS)
• The presence of VLPs is indicative of ?risk for
  subsequent occurrence of arrhythmic events,
  mainly SuVT

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An HRECG instrument consists of 4 key components
1) Amplifiers, 2) Bandpass filters, 3)
Analog/Digital converter, and 4) SAECG Processor.
The SAECG Processor may in turn be functionally
divided into the following components a)
Signal Averager, b) Bidirectional Bandpass
Filter, c) Filtered Vector Magnitude, d) SAECG
Quantifier. In addition, the instrument
includes 7 ECG leads. These leads are bipolar,
orthogonal electrodes comprising X, X-, Y, Y-,
Z, Z-, ground placed in a particular fashion
on the body surface. These electrodes are
usually referred to as XYZ leads.
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SAECG

• Signal-averaged electrocardiography (SAECG) is a
  technique involving computerized analysis of
  segments of a standard surface electrocardiogram.
  
• It is used for detecting small electrical
  impulses, termed ventricular late potentials,
  that follow the QRS segment.
• These signals are embedded in the
  electrocardiogram but ordinarily obscured by
  skeletal muscle activity and other extraneous
  sources of "noise" encountered in recording a
  standard electrocardiogram.

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Late Potentials

• Ventricular late potentials in patients with
  cardiac abnormalities, especially coronary artery
  disease or following an acute myocardial
  infarction, are associated with an increased risk
  of ventricular tachyarrhythmias and sudden
  cardiac death.
• Proponents of SAECG claim that it can obviate the
  need for invasive techniques commonly used to
  identify high-risk patients for interventions
  that treat or prevent ventricular tachyarrhythmia
  and sudden death.

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SAECG

• The current data on SAECG show relatively
  consistent high negative predictive values, poor
  positive predictive values, and variable
  sensitivity and specificity when the technique is
  used on pts with CM or following a MI
• The available evidence also indicates that
  combining SAECG with other tests of cardiac
  function is superior to using any single test for
  risk.
• The utility of SAECG alone as an indicator of
  risk remains to be proven.
• SAECG combined with other standard tests of risk
  has been demonstrated to have clinical utility in
  patients following an acute myocardial
  infarction.
• Other patient populations have not been
  conclusively shown to benefit from its use.

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SAECG Normal (left) and abnormal (right)
results are shown from a patient with prior MI
and VT. Bottom panels Shaded blue areas at the
end of each tracing represent voltage content of
last 40 ms of the filtered QRS integral. The
small shaded area in the abnormal study denotes
prolonged, slow conduction and suggests the
potential for reentrant ventricular arrhythmias.
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Late Potentials

• One of the constituents of reentrant ventricular
  arrhythmias in patients with prior myocardial
  damage is slow conduction.
• Direct cardiac mapping techniques can record
  myocardial activation from damaged areas that
  occurs after the end of the surface ECG QRS
  complex during sinus rhythm.
• These delayed signals have very low amplitude
  that cannot be discerned on routine ECG and
  correspond to the delayed and fragmented
  conduction in the ventricles recorded with direct
  mapping techniques

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SAECG

• Signal averaging has been applied clinically most
  often to detect such late ventricular potentials
  of 1 to 25 µV
• Criteria for late potentials are
• (1) filtered QRS complex duration (QRSD) gt114
  120 ms,
• (2) lt 20 µV of root-mean-square (RMS) signal
  amplitude in the last 40 ms of the filtered QRS
  complex, and
• (3) the terminal filtered QRS complex remains
  below 40 µV (low amplitude signal-LAS) for longer
  than 38 ms

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Time-Domain Analysis Results of most studies
have been based on analysis of a vector magnitude
of the filtered leads, vx2y2 z2, called the
filtered QRS complex. The end of the filtered
QRS complex is defined as the midpoint of a 5
msec segment in which mean voltage exceeds the
mean noise level plus 3 times the standard
deviation of the noise sample. The end point and
onset of the filtered QRS complex should be
verified visually, and the system should allow
manual adjustment of the automatically
determined end points.
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SAECG Criteria

• Analysis should include determination of
• 1) the filtered QRS duration
• 2) root mean square voltage of the terminal 40
  msec of the filtered QRS and
• 3) amount of time that the filtered QRS complex
  remains below 40 µV
• For the definition of a late potential and the
  scoring of a high-resolution ECG as normal or
  abnormal
• Representative criteria include that a late
  potential exists (using 40 Hz high-pass
  bidirectional filtering) when
• 1) the filtered QRS complex is greater than 114
  msec,
• 2) there is less than 20 µV of signal in the last
  40 msec of the vector magnitude complex, and
• 3) the terminal vector magnitude complex remains
  below 40 µV for more than 38 msec

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Late Potentials

• These late potentials have been recorded in 70
  90 of patients with spontaneous sustained and
  inducible VT after myocardial infarction,
• in only 0 to 6 of normal volunteers, and
• in 7 to 15 of patients after myocardial
  infarction who do not have VT

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Late Potentials

• Late potentials can be detected as early as 3 h
  after the onset of chest pain, increase in
  prevalence in the first week after MI, and
  disappear in some patients after 1 year.
• If not present initially, late potentials usually
  do not appear later.
• Early use of thrombolytic agents may reduce the
  prevalence of late potentials after coronary
  occlusion
• Patients with BBB or paced ventricular rhythms
  have wide QRS complexes already, rendering the
  technique less useful in these cases.

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Late Potentials

• Late potentials also have been recorded in
  patients with VT not related to ischemia, such as
  dilated cardiomyopathies.
• Successful surgical resection of the VT can
  eliminate late potentials but is not necessary to
  cause tachycardia suppression.
• The presence of a late potential is a sensitive,
  but not specific, marker of arrhythmic risk and
  thus its prognostic use is limited

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Late Potentials

• In specific situations, LPs can be helpful
• for instance, a patient with a prior inferior
  wall myocardial infarction (normally the last
  portion of the heart to be activated) who has no
  late potential has a very low likelihood of
  having VT episodes.

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SAECG/ Time Domain Analysis

• The high-pass filtering used to record late
  potentials meeting the criteria just noted is
  called time domain analysis because the filter
  output corresponds in time to the input signal.
• Because late potentials are high-frequency
  signals, Fourier transform can be applied to
  extract high-frequency content from the
  signal-averaged ECG, called frequency domain
  analysis.

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SAECG/ Frequency Analysis

• A sequence generated by sampling a time-domain
  signal like the ECG can be represented in the
  frequency domain by taking the fast Fourier
  transform.
• Even though the informational content in time-
  and frequency-domain representations of a
  periodic waveform is equivalent, the extent to
  which each can depict components of interest
  depends on the signal being analyzed.
• The Fourier transform is a complete description
  of the ECG and contains information that may not
  be seen in the output of a particular fixed-band
  filter.
• Frequency analysis offers potential advantages
  for identification and characterization of
  signals that differentiate patients with from
  those without sustained ventricular tachycardia.
• Most studies have calculated the fast Fourier
  transform to estimate scalar-lead spectra of the
  terminal QRS and ST segment of signal-averaged
  Frank X, Y, Z or uncorrected orthogonal
  leads.11-16,47,59
• The results have often been expressed as indexes
  of the relative contributions of specific
  frequencies that comprise these ECG segments.
• A window function such as the four-term
  Blackman-Harris window has been used to diminish
  spectral leakage caused by edge discontinuities.

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SAECG/ Frequency Analysis

• Key issues that affect the spectra of ECG signals
  are being investigated. For example, the
  frequency content of ECG signals is spatially
  variable and thus lead dependent.
• Indexes derived from spectra of uncorrected leads
  may not be comparable to end points or approaches
  developed using corrected leads.
• Analysis of multiple segments (spectrotemporal
  mapping) may allow better separation between
  noise and late potentials
• The value of autoregressive models of spectral
  estimation and analysis of the entire cardiac
  cycle with methods that obviate window functions
  are currently being determined.6263
• Accordingly, the committee believes it is
  premature to standardize this approach at present

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SAECG

• Some data suggest that frequency domain analysis
  provides useful information not available in the
  time domain analysis.
• Signal averaging has been applied to the P wave
  to determine risk for developing atrial
  fibrillation as well as maintenance of sinus
  rhythm after cardioversion. The overall use of
  the technique remains limited at present.

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A 90 year old lady with syncope
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SAECG/Noise

• noise be measured in the averaged signal over an
  interval of at least 40 msec in the ST or TP
  segment with a four-pole Butterworth filter.
• With this approach, noise should be lt 1 µV with a
  25 Hz high-pass cutoff or
• lt 0.7 µV with a 40 Hz high-pass cutoff as
  measured by the root mean square method from a
  vector magnitude of the X, Y, and Z leads.
• The segment for noise level analysis should be
  determined automatically
• The inherent noise level of the recording should
  be low so that adequate noise reduction can be
  achieved by averaging 50-300 beats.
• Averaging a greater number of beats to obtain
  adequate noise reduction indicates that baseline
  noise is excessive for optimum recording.

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SAECG / Indications

• Vulnerability to SuVT ? risk stratification
  post-MI
• Unexplained syncope
• Post-operative patients

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Heart Rate Variability-HRV

• Heart rate variability has become the
  conventionally accepted term to describe
  variations of both instantaneous heart rate RR
  intervals
• oscillation in the interval between consecutive
  heartbeats as well as the oscillations between
  consecutive instantaneous heart rates
• the interval between consecutive beats is being
  analyzed rather than the heart rate per se

Measurement of HRV

• Time Domain Methods
• Continuous ECG record each QRS complex is
  detected, so-called normal-to-normal (NN)
  intervals or the instantaneous HR is determined
• Simple time domain variables mean NN interval,
  mean heart rate, difference between the longest
  shortest NN interval, difference between night
  day heart rate, etc

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HRV

• Simplest variable to calculate the SD of the NN
  intervals (SDNN), that is, the square root of
  variance. Since variance is mathematically equal
  to total power of spectral analysis, SDNN
  reflects all the cyclic components responsible
  for variability in the period of recording
• short-term 5-min recordings nominal 24-h
  long-term recordings appear to be appropriate
  options
• Other commonly used statistical variables
  SDANN?SD of the average NN intervals calculated
  over 5 min (an estimate of changes in HR due to
  cycles gt5 min), the SDNN index ? mean of the
  5-min SDs of NN intervals calculated over 24 h
  (variability due to cycles lt 5 min)
• Most commonly used measures derived fm interval
  differences include RMSSD ?square root of the
  mean squared differences of successive NN
  intervals, NN50 ?the number of interval
  differences of successive NN intervals gt50 ms,
  pNN50 ? the proportion derived by dividing NN50
  by the total number of NN intervals. All of these
  measurements of short-term variation estimate
  high-frequency variations in heart rate thus
  are highly correlated

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Relationship between the RMSSD and pNN50 (a)
and pNN50 and NN50 (b) measures of HRV
assessed from 857 nominal 24-hour Holter tapes
recorded in survivors of acute MI before hospital
discharge. The NN50 measure used in b was
normalized in respect to the length of the
recording
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To perform geometric measures on the NN interval
histogram, the sample density distribution D is
constructed, which assigns the number of equally
long NN intervals to each value of their lengths.
The most frequent NN interval length X is
established, that is, YD(X) is the maximum of
the sample density distribution D. The HRV
triangular index is the value obtained by
dividing the area integral of D by the maximum Y.
When the distribution D with a discrete scale is
constructed on the horizontal axis, the value is
obtained according to the formula HRV
index(total number of all NN intervals)/Y. For
the computation of the TINN measure, the values N
and M are established on the time axis and a
multilinear function q constructed such that
q(t)0 for tN and tM and q(X)Y, and such that
the integral ?08 (D(t)-q(t))2 dt is the minimum
among all selections of all values N and M. The
TINN measure is expressed in milliseconds and
given by the formula TINNM-N.
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Selected Time Domain Measures of HRV
Since many of the measures correlate closely with
others, the following 4 measures are recommended
(1) SDNN (estimate of overall HRV), (2) HRV
triangular index (estimate of overall HRV), (3)
SDANN (estimate of long-term components of HRV),
and (4) RMSSD (estimate of short-term components
of HRV) 2 estimates of the overall HRV are
recommended because the HRV triangular index
permits only casual preprocessing of the ECG
signal. The RMSSD method is preferred to pNN50
NN50 because it has better statistical properties
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Frequency Domain Analysis

• Power spectral density (PSD) analysis provides
  the basic information of how power (variance)
  distributes as a function of frequency.
• Short-term recordings 3 main spectral components
  are distinguished in a spectrum calculated from
  short-term recordings of 2 to 5 min VLF, LF,
  HF components
• Measurement of VLF, LF, HF is made in absolute
  values of power (ms squared). LF HF may also be
  measured in normalized units, which represent the
  relative value of each power component in
  proportion to the total power minus the VLF
• Long-term recordings. Spectral analysis also may
  be used to analyze the sequence of NN intervals
  of the entire 24- period. The result then
  includes a ULF component, in addition to VLF, LF,
  HF components. The slope of the 24-h spectrum
  also can be assessed on a log-log scale by linear
  fitting the spectral values
• Although time domain methods, esp. SDNN RMSSD,
  can be used to investigate recordings of short
  durations / frequency methods are usually able to
  provide results that are more easily
  interpretable in terms of physiological
  regulations.
• In general, time domain methods are ideal for the
  analysis of long-term recordings
• Substantial part of the long-term HRV value is
  contributed by the day-night differences. Thus,
  the long-term recording analyzed by the time
  domain methods should contain at least 18 hours
  of analyzable ECG data that include the whole
  night

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Spectral analysis of RR interval variability in a
healthy subject at rest during 90 head-up
tilt.
At rest, 2 major components of similar power are
detectable at low and high frequencies. During
tilt, the LF component becomes dominant, but as
total variance is reduced, the absolute power of
LF appears unchanged compared with rest.
Normalization procedure leads to predominant LF
smaller HF components, which express the
alteration of spectral components due to tilt.
Circulation 1996931043-1065
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Selected Frequency Domain Measures of HRV
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Example of an estimate of power spectral density
obtained from the entire 24-hour interval of a
long-term Holter recording
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Approximate Correspondence of Time Domain and
Frequency Domain Methods Applied to 24-Hour ECG
Recordings
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Physiological Correlates of HRV

• Vagal activity is the major contributor to the HF
  component.
• Disagreement exists in respect to the LF
  component. Some studies suggest that LF, when
  expressed in normalized units, is a quantitative
  marker of sympathetic modulations other studies
  view LF as reflecting both sympathetic activity
  and vagal activity. Consequently, the LF/HF ratio
  is considered by some investigators to mirror
  sympathovagal balance or to reflect the
  sympathetic modulations.
• Physiological interpretation of lower-frequency
  components of HRV (that is, of the VLF and ULF
  components) warrants further elucidation.
• It is important to note that HRV measures
  fluctuations in autonomic inputs to the heart
  rather than the mean level of autonomic inputs.
  Thus, both autonomic withdrawal and saturatingly
  high level of sympathetic input lead to
  diminished HRV

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Changes of HRV Related to Specific Pathologies

• MI
• ?HRV after MI may reflect a ? in vagal activity
  directed to the heart, which leads to prevalence
  of sympathetic mechanisms and to cardiac
  electrical instability. In the acute phase of MI,
  the ? in 24-hour SDNN is significantly related to
  LV dysfunction, peak CK, Killip class
• an ? LF a ? HF were observed during both
  resting controlled conditions 24-hour
  recordings analyzed over multiple 5-min periods
• These changes may indicate a shift of
  sympathovagal balance toward a sympathetic
  predominance and a ? vagal tone
• Diabetic Neuropathy
• a ? in time domain parameters of HRV seems not
  only to carry negative prognostic value but also
  to precede the clinical expression of autono-mic
  neuropathy / the initial manifestation of this
  neuropathy is likely to involve both efferent
  limbs of ANS (no change in spectral analysis)
• Heart Failure
• A ? HRV has been observed consistently in pts
  with HF

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Modifications of HRV by Specific Interventions

• The rationale for trying to modify HRV after MI
  stems from the multiple observations indicating
  that cardiac mortality is higher among those
  post-MI patients who have a more depressed HRV
• The inference is that interventions that augment
  HRV may be protective against cardiac mortality
  and sudden cardiac death.
• Although the rationale for changing HRV is sound,
  it also contains the inherent danger of leading
  to the unwarranted assumption that modification
  of HRV translates directly into cardiac
  protection, which may not be the case
• The target is the improvement of cardiac
  electrical stability, and HRV is just a marker of
  autonomic activity
• ß-Adrenergic Blockade and HRV
• AADs HRV some AADs a/w increased mortality can
  reduce HRV
• Scopolamine
• Thrombolysis
• Exercise training

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Clinical Use of HRV

• General consensus of practical use of HRV has
  been reached only in 2 clinical scenarios ? HRV
  can be used as a predictor of risk after AMI as
  an early warning sign of DM neuropathy

Cumulative survival of pts after MI a, Survival
of pts stratified according to 24-h SDNN values
into 3 groups with cutoff points of 50 100 ms
b, Similar survival curves of pts stratified
according to 24-h HRV triangular index values
with cutoff points of 15 20 units, respectively
Data suggest that ? HRV is not a simple
reflection of sympathetic overdrive and/or vagal
withdrawal due to poor LV performance but that it
also reflects ? vagal activity, which has a
strong association with the pathogenesis of
ventricular arrhythmias sudden cardiac death
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Predictive value of HRV
The predictive value of HRV alone is modest.
Combination with other techniques substantially
improves the positive predictive accuracy of HRV
over a clinically important range of sensitivity
(25 to 75) for cardiac mortality arrhythmic
events
Comparison of positive predictive characteristics
of HRV (solid lines) of combinations of HRV
with LVEF (dashed lines) of HRV with LVEF
ectopic counts on 24-hour ECGs (dotted lines)
used for identification of pts at risk of 1-year
cardiac mortality (a) 1-year arrhythmic events
(SCD and/or symptomatic suVT, b) after acute MI
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Interpreting HRV

• Depressed HRV After Acute MI
• ?HRV is a predictor of mortality arrhythmic
  complications that is independent of other
  recognized risk factors / measured 1 wk pMI
• HRV assessed from short-term recordings provides
  prognostic information (initial screening ), but
  HRV measured in 24-h recordings is a stronger
  risk predictor
• Better prognosis c time domain HRV measures (SDNN
  or HRV triangular index). Some other measures,
  e.g, ULF of entire 24-h spectral analysis,
  perform equally well. A high-risk gp may be
  selected by the dichotomy limits of SDNN lt50 ms
  or HRV triangular index lt15
• Predictive value of HRV alone is modest, but
  higher than that of any other recognized risk
  factor. To improve the predictive value, HRV may
  be combined with other factors
• Assessment of Diabetic Autonomic Neuropathy (DAN)
  
• Characterized by early widespread neuronal
  degeneration of small nerve fibers of both
  sympathetic parasympathetic tracts.
  Manifestations postural hypotension, pers.t
  tachycardia, gustatory sweating, gastroparesis,
  bladder atony, nocturnal diarrhea. Once DAN
  supervenes, 5-y mortality is 50. Thus, early
  subclinical detection is important
• There are 3 HRV methods from which to choose (1)
  simple bedside RR interval methods, (2) long-term
  time domain measures that are more sensitive and
  more reproducible than the short-term tests, and
  (3) frequency domain analysis performed under
  short-term steady state conditions, which is
  useful in separating sympathetic from
  parasympathetic abnormalities