ECG Rhythm Interpretation (ECG Rhythm Analysis)

ECG Rhythm

1. ECG Rhythm Interpretation
Module II

2. ECG Rhythm Analysis
•
Step 1:
Calculate rate.
•
Step 1:
Calculate rate.
•
Step 2: Determine regularity.
•
Step 3: Assess the P waves.
•
Step 4: Determine PR interval.
•
Step 5: Determine QRS duration.

3. Step 1: Calculate Rate •
Option 1
3 sec
3 sec
•
Option 1
–
Count the # of R waves in a 6 second
rhythm strip, then multiply by 10.
–
Reminder: all rhythm strips in the Modules
are 6 seconds in length.
Interpretation?
9 x 10 = 90 bpm

4. Step 2: Determine regularity •
Look at the R
-
R distances (using a caliper or
R R
•
Look at the R
-
R distances (using a caliper or
markings on a pen or paper).
•
Regular (are they equidistant apart)?
Occasionally irregular? Regularly irregular?
Irregularly irregular?
Interpretation?
Regular

5. Step 3: Assess the P waves •
Are there P waves?
•
Are there P waves?
•
Do the P waves all look alike?
•
Do the P waves occur at a regular rate?
•
Is there one P wave before each QRS?
Interpretation?
Normal P waves with 1 P
wave for every QRS

6. Step 4: Determine PR interval •
Normal: 0.12
-
0.20 seconds.
•
Normal: 0.12
-
0.20 seconds.
(3 -5 boxes)
Interpretation?
0.12 seconds

7. Step 5: QRS duration •
Normal: 0.04
-
0.12 seconds.
•
Normal: 0.04
-
0.12 seconds.
(1 -3 boxes)
Interpretation?
0.08 seconds

8. Rhythm Summary
•
Rate
90
-
95 bpm
•
Rate
90
-
95 bpm
•
Regularity regular
•
P waves normal
•
PR interval 0.12 s
•
QRS duration 0.08 s
Interpretation?
Normal Sinus Rhythm

9. Normal Sinus Rhythm (NSR)
•
Etiology:
the electrical impulse is formed
in the SA node and conducted normally. in the SA node and conducted normally.
•
This is the normal rhythm of the heart;
other rhythms that do not conduct via
the typical pathway are called
arrhythmias.

10. NSR Parameters
•
Rate
60 -100 bpm
•
Regularity
regular
•
P waves
normal
•
PR interval
0.12 -0.20 s
•
QRS duration
0.04 -0.12 s
Any deviation fromabove is sinus tachycardia,
sinusbradycardiaoranarrhythmia

11. ECG Rhythm Interpretation
Module III
Normal Sinus Rhythm

12. Course Objectives
•To recognize the normal rhythm of the
heart -“Normal Sinus Rhythm.”
•
To recognize the 13 most common
•
To recognize the 13 most common rhythm disturbances.
•
To recognize an acute myocardial
infarction on a 12-lead ECG.

13. Learning Modules
•ECG Basics
•
How to Analyze a Rhythm
•
Normal Sinus Rhythm
•
Heart Arrhythmias
•
Diagnosing a Myocardial Infarction
•
Advanced 12-Lead Interpretation

14. Normal Sinus Rhythm (NSR)
•
Etiology:
the electrical impulse is formed
in the SA node and conducted normally. in the SA node and conducted normally.
•
This is the normal rhythm of the heart;
other rhythms that do not conduct via
the typical pathway are called
arrhythmias.

15. NSR Parameters
•
Rate
60 -100 bpm
•
Regularity
regular
•
P waves
normal
•
PR interval
0.12 -0.20 s
•
QRS duration
0.04 -0.12 s
Any deviation from above is sinus tachycardia,
sinus bradycardiaor an arrhythmia

16. Arrhythmia Formation
Arrhythmias can arise from problems in
the:
•
Sinus node
•
Sinus node
•
Atrial cells
•
AV junction
•
Ventricular cells

17. SA Node Problems
The SA Node can: •
fire too slow
•
fire too fast
Sinus Bradycardia Sinus Tachycardia
•
fire too fast
Sinus Tachycardia
Sinus Tachycardia may be an appropriate
response to stress.

18. Atrial Cell Problems
Atrial cells can: •
fire occasionally from a focus
Premature Atrial
Contractions (PACs)
from a focus
•
fire continuously
due to a looping
re-entrant circuit
Contractions (PACs)
Atrial Flutter

19. Teaching Moment
•A re-entrant
pathway occurs
when an impulse when an impulse loops and results
in self-
perpetuating
impulse
formation.

20. Atrial Cell Problems
Atrial cells can also:
• fire continuously
from multiple foci
Atrial Fibrillation
from multiple foci or
fire continuously
due to multiple
micro re-entrant
“wavelets”
Atrial Fibrillation

21. AV Junctional Problems
The AV junction can: •
fire continuously due to a looping
Paroxysmal
Supraventricular
due to a looping re-entrant circuit
•
block impulses
coming from the
SA Node
Supraventricular Tachycardia
AV Junctional Blocks

22. Ventricular Cell Problems
Ventricular cells can: •
fire occasionally from 1 or more foci
Premature Ventricular
Contractions (PVCs)
from 1 or more foci
•
fire continuously
from multiple foci
•
fire continuously
due to a looping
re-entrant circuit
Contractions (PVCs)
Ventricular Fibrillation
Ventricular Tachycardia

23. ECG Rhythm Interpretation
Module IV a

24. Rhythm #1
30 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular normal
0.10 s
• P waves?
• PR interval?
0.12 s
• QRS duration?
Interpretation?
Sinus Bradycardia

25. Sinus Bradycardia
•
Etiology:
SA node is depolarizing slower
than normal, impulse is conducted than normal, impulse is conducted normally (i.e. normal PR and QRS
interval).

26. Rhythm #2
130 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular normal
0.08 s
• P waves?
• PR interval?
0.16 s
• QRS duration?
Interpretation?
Sinus Tachycardia

27. Sinus Tachycardia
•
Etiology:
SA node is depolarizing faster
than normal, impulse is conducted than normal, impulse is conducted normally.
•
Remember: sinus tachycardia is a
response to physical or psychological
stress, not a primary arrhythmia.

28. Rhythm #3
70 bpm
• Rate? •
Regularity?
occasionally irreg.
•
Regularity?
occasionally irreg. 2/7 different contour
0.08 s
• P waves?
• PR interval?
0.14 s (except 2/7)
• QRS duration?
Interpretation?
NSR with Premature Atrial
Contractions

29. Premature Atrial Contractions •
Deviation from NSR
•
Deviation from NSR
–
These ectopic beats originate in the
atria (but not in the SA node),
therefore the contour of the P wave,
the PR interval, and the timing are
different than a normally generated
pulse from the SA node.

30. Premature Atrial Contractions
•
Etiology:
Excitation of an atrial cell
forms an impulse that is then conducted forms an impulse that is then conducted normally through the AV node and
ventricles.

31. Rhythm #4
60 bpm
• Rate? •
Regularity?
occasionally irreg.
•
Regularity?
occasionally irreg. none for 7thQRS
0.08 s (7th wide)
• P waves?
• PR interval?
0.14 s
• QRS duration?
Interpretation?
Sinus Rhythm with 1 PVC

32. PVCs
•
Deviation from NSR
–
Ectopic beats originate in the ventricles
–
Ectopic beats originate in the ventricles resulting in wide and bizarre QRS
complexes.
–
When there are more than 1 premature
beats and look alike, they are called
“uniform”. When they look different, they are
called “multiform”.

33. Ventricular Conduction Normal
Signal moves rapidly
through the ventricles
Abnormal
Signal moves slowly
through the ventricles

34. ECG Rhythm Interpretation
Module IV b
Supraventricular and
Ventricular Arrhythmias

35. Course Objectives
•To recognize the normal rhythm of the
heart -“Normal Sinus Rhythm.”
•
To recognize the 13 most common
•
To recognize the 13 most common rhythm disturbances.
•
To recognize an acute myocardial
infarction on a 12-lead ECG.

36. Learning Modules
•ECG Basics
•
How to Analyze a Rhythm
•
Normal Sinus Rhythm
•
Heart Arrhythmias
•
Diagnosing a Myocardial Infarction
•
Advanced 12-Lead Interpretation

37. Arrhythmias
•
Sinus Rhythms
•
Premature Beats
•
Supraventricular Arrhythmias
•
Ventricular Arrhythmias
•
AV Junctional Blocks

38. Supraventricular Arrhythmias
•
Atrial Fibrillation
•
Atrial Flutter
•
Atrial Flutter
•
Paroxysmal Supraventricular
Tachycardia

39. Rhythm #5
100 bpm
• Rate? •
Regularity?
irregularly irregular
•
Regularity?
irregularly irregular none
0.06 s
• P waves?
• PR interval?
none
• QRS duration?
Interpretation?
Atrial Fibrillation

40. Atrial Fibrillation
•Deviation from NSR
–
No organized atrial depolarization, so no normal P waves (impulses are not no normal P waves (impulses are not originating from the sinus node).
–
Atrial activity is chaotic (resulting in an
irregularly irregular rate).
–
Common, affects 2-4%, up to 5-10% if
> 80 years old

41. Atrial Fibrillation
•Etiology:
Recent theories suggest that it
is due to multiple re-entrant wavelets
conducted between the R & L atria. conducted between the R & L atria. Either way, impulses are formed in a
totally unpredictable fashion. The AV
node allows some of the impulses to
pass through at variable intervals (so
rhythm is irregularly irregular).

42. Rhythm #6
70 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular flutter waves
0.06 s
• P waves?
• PR interval?
none
• QRS duration?
Interpretation?
Atrial Flutter

43. Atrial Flutter
•
Deviation from NSR
–
No P waves. Instead flutter waves (note
–
No P waves. Instead flutter waves (note “sawtooth” pattern) are formed at a rate
of 250 -350 bpm.
–
Only some impulses conduct through
the AV node (usually every other
impulse).

44. Atrial Flutter
•
Etiology:
Reentrant pathway in the right
atrium with every 2nd, 3rd or 4th atrium with every 2nd, 3rd or 4th impulse generating a QRS (others are
blocked in the AV node as the node
repolarizes).

45. Rhythm #7
74 148bpm
• Rate? •
Regularity?
Regular

regular
•
Regularity?
Regular

regular
Normal none
0.08 s
• P waves?
• PR interval?
0.16 s none
• QRS duration?
Interpretation?
Paroxysmal Supraventricular
Tachycardia (PSVT)

46. PSVT
•
Deviation from NSR
–
The heart rate suddenly speeds up,
–
The heart rate suddenly speeds up, often triggered by a PAC (not seen
here) and the P waves are lost.

47. PSVT
•
Etiology:
There are several types of
PSVT but all originate above the PSVT but all originate above the ventricles (therefore the QRS is narrow).
•
Most common: abnormal conduction in
the AV node (reentrant circuit looping in
the AV node).

48. Ventricular Arrhythmias
•
Ventricular Tachycardia
•
Ventricular Fibrillation
•
Ventricular Fibrillation

49. Rhythm #8
160 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular none
wide (> 0.12 sec)
• P waves?
• PR interval?
none
• QRS duration?
Interpretation?
Ventricular Tachycardia

50. Ventricular Tachycardia
•
Deviation from NSR
–
Impulse is originating in the ventricles
–
Impulse is originating in the ventricles (no P waves, wide QRS).

51. Ventricular Tachycardia
•
Etiology:
There is a re-entrant pathway
looping in a ventricle (most common looping in a ventricle (most common cause).
•
Ventricular tachycardia can sometimes
generate enough cardiac output to
produce a pulse; at other times no pulse
can be felt.

52. Rhythm #9
none
• Rate? •
Regularity?
irregularly irreg.
•
Regularity?
irregularly irreg. none
wide, if recognizable
• P waves?
• PR interval?
none
• QRS duration?
Interpretation?
Ventricular Fibrillation

53. Ventricular Fibrillation
•
Deviation from NSR
–
Completely abnormal.
–
Completely abnormal.

54. Ventricular Fibrillation
•
Etiology:
The ventricular cells are
excitable and depolarizing randomly. excitable and depolarizing randomly.
•
Rapid drop in cardiac output and death
occurs if not quickly reversed

55. ECG Rhythm Interpretation
Module IV c
AV Junctional Blocks

56. Course Objectives
•To recognize the normal rhythm of the
heart -“Normal Sinus Rhythm.”
•
To recognize the 13 most common
•
To recognize the 13 most common rhythm disturbances.
•
To recognize an acute myocardial
infarction on a 12-lead ECG.

57. Learning Modules
•ECG Basics
•
How to Analyze a Rhythm
•
Normal Sinus Rhythm
•
Heart Arrhythmias
•
Diagnosing a Myocardial Infarction
•
Advanced 12-Lead Interpretation

58. Arrhythmias
•
Sinus Rhythms
•
Premature Beats
•
Supraventricular Arrhythmias
•
Ventricular Arrhythmias
•
AV Junctional Blocks

59. AV Nodal Blocks
•
1st Degree AV Block
•
2nd Degree AV Block, Type I
•
2nd Degree AV Block, Type I
•
2nd Degree AV Block, Type II
•
3rd Degree AV Block

60. Rhythm #10
60 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular normal
0.08 s
• P waves?
• PR interval?
0.36 s
• QRS duration?
Interpretation?
1st Degree AV Block

61. 1st Degree AV Block
•
Deviation from NSR
–
PR Interval
> 0.20 s
–
PR Interval
> 0.20 s

62. 1st Degree AV Block
•
Etiology:
Prolonged conduction delay in
the AV node or Bundle of His. the AV node or Bundle of His.

63. Rhythm #11
50 bpm
• Rate? •
Regularity?
regularly irregular
•
Regularity?
regularly irregular nl, but 4th no QRS
0.08 s
• P waves?
• PR interval?
lengthens
• QRS duration?
Interpretation?
2nd Degree AV Block, Type I

64. 2nd Degree AV Block, Type I
•
Deviation from NSR
–
PR interval progressively lengthens,
–
PR interval progressively lengthens, then the impulse is completely blocked
(P wave not followed by QRS).

65. 2nd Degree AV Block, Type I
•
Etiology:
Each successive atrial impulse
encounters a longer and longer delay in encounters a longer and longer delay in the AV node until one impulse (usually
the 3rd or 4th) fails to make it through
the AV node.

66. Rhythm #12
40 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular nl, 2 of 3 no QRS
0.08 s
• P waves?
• PR interval?
0.14 s
• QRS duration?
Interpretation?
2nd Degree AV Block, Type II

67. 2nd Degree AV Block, Type II
•
Deviation from NSR
–
Occasional P waves are completely
–
Occasional P waves are completely blocked (P wave not followed by QRS).

68. 2nd Degree AV Block, Type II
•
Etiology:
Conduction is all or nothing
(no prolongation of PR interval); (no prolongation of PR interval); typically block occurs in the Bundle of
His.

69. Rhythm #13
40 bpm
• Rate? •
Regularity?
regular
•
Regularity?
regular no relation to QRS
wide (> 0.12 s)
• P waves?
• PR interval?
none
• QRS duration?
Interpretation?
3rd Degree AV Block

70. 3rd Degree AV Block
•
Deviation from NSR
–
The P waves are completely blocked in
–
The P waves are completely blocked in the AV junction; QRS complexes
originate independently from below the
junction.

71. 3rd Degree AV Block
•
Etiology:
There is complete block of
conduction in the AV junction, so the conduction in the AV junction, so the atria and ventricles form impulses
independently of each other. Without
impulses from the atria, the ventricles
own intrinsic pacemaker kicks in at
around 30 -45 beats/minute.

72. Remember
•
When an impulse originates in a ventricle,
conduction through the ventricles will be
inefficient and the QRS will be wide and
bizarre. bizarre.

73. ECG Rhythm Interpretation
Module V
Acute Myocardial Infarction

74. Course Objectives
•To recognize the normal rhythm of the
heart -“Normal Sinus Rhythm.”
•
To recognize the 13 most common
•
To recognize the 13 most common heart arrhythmias.
•
To recognize an acute myocardial
infarction on a 12-lead ECG.

75. Learning Modules
•ECG Basics
•
How to Analyze a Rhythm
•
Normal Sinus Rhythm
•
Heart Arrhythmias
•
Diagnosing a Myocardial Infarction
•
Advanced 12-Lead Interpretation

76. Diagnosing a MI
To diagnose a myocardial infarction you
need to go beyond looking at a rhythm
strip and obtain a 12-Lead ECG.
Rhythm
Strip
12-Lead
ECG

77. The 12-Lead ECG
•The 12-Lead ECG sees the heart
from 12 different views.
•
Therefore, the 12
-
Lead ECG helps
•
Therefore, the 12
-
Lead ECG helps
you see what is happening in
different portions of the heart.
•
The rhythm strip is only 1 of these 12
views.

78. The 12-Leads
The 12-leads include:
–
3 Limb leads
(I, II, III) (I, II, III)
–
3 Augmented leads
(aVR, aVL, aVF)
–
6 Precordial leads
(V
1-V
6)

79. Views of the Heart
Some leads get a
good view of the:
Lateral portion
of the heart
Anterior portion
of the heart
Inferior portion
of the heart

80. ST Elevation
One way to
diagnose an
acute MI is to acute MI is to look for
elevation of
the ST
segment.

81. ST Elevation (cont)
Elevation of the
ST segment
(greater than 1 (greater than 1 small box) in 2
leads is
consistent with a
myocardial
infarction.

82. Anterior View of the Heart
The anterior portion of the heart is best
viewed using leads V
1-V
4.

83. Anterior Myocardial Infarction
If you see changes in leads V
1-V
4
that are consistent with a myocardial
infarction, you can conclude that it is infarction, you can conclude that it is an anterior wall myocardial infarction.

84. Putting it all Together
Do you think this person is having a
myocardial infarction. If so, where?

85. Interpretation
Yes
, this person is having an acute anterior
wall myocardial infarction.

86. Other MI Locations
Now that you know where to look for an
anterior wall myocardial infarction let’s
look at how you would determine if the MI look at how you would determine if the MI involves the lateral wall or the inferior wall
of the heart.

87. Other MI Locations
First, take a look
again at this
picture of the heart.
Lateral portion
of the heart
Anterior portion
of the heart
Inferior portion
of the heart

88. Other MI Locations
Second, remember that the 12-leads of the ECG look at
different portions of the heart. The limb and augmented
leads “see” electrical activity moving inferiorly ( II, III and
aVF), to the left (I, aVL) and to the right (aVR). Whereas, the
precordial leads “see” electrical activity in the posterior to
anterior direction. anterior direction.
Limb Leads Augmented Leads Precordial Leads

89. Other MI Locations
Now, using these 3 diagrams let’s figure where
to look for a lateral wall and inferior wall MI.
Limb Leads
Augmented Leads
Precordial Leads
Limb Leads
Augmented Leads
Precordial Leads

90. Anterior MI
Remember the anterior portion of the heart is
best viewed using leads V
1
-V
4
.
Limb Leads
Augmented Leads
Precordial Leads
Limb Leads
Augmented Leads
Precordial Leads

91. Lateral MI
So what leads do you think
the lateral portion of the
heart is best viewed?
Limb Leads
Augmented Leads
Precordial Leads
Leads I, aVL, and V
5-V
6
Limb Leads
Augmented Leads
Precordial Leads

92. Inferior MI
Now how about the
inferior portion of the
heart?
Limb Leads
Augmented Leads
Precordial Leads
Leads II, III and aVF
Limb Leads
Augmented Leads
Precordial Leads

93. Putting it all Together
Now, where do you think this person is
having a myocardial infarction?

94. Inferior Wall MI
This is an inferior MI. Note the ST elevation
in leads II, III and aVF.

95. Putting it all Together
How about now?

96. Anterolateral MI
This person’s MI involves
both
the anterior wall
(V
2
-V
4
) and the lateral wall (V
5
-V
6
, I, and aVL)!

97. ECG Rhythm Interpretation
Module VI
Advanced 12-Lead Interpretation

98. Course Objectives
•To recognize the normal rhythm of the
heart -“Normal Sinus Rhythm.”
•
To recognize the 13 most common
•
To recognize the 13 most common heart arrhythmias.
•
To recognize an acute myocardial
infarction on a 12-lead ECG.

99. Learning Modules
•ECG Basics
•
How to Analyze a Rhythm
•
Normal Sinus Rhythm
•
Heart Arrhythmias
•
Diagnosing a Myocardial Infarction
•
Advanced 12-Lead Interpretation

100. The 12-Lead ECG
The 12-Lead ECG contains a wealth of
information. In Module V you learned that
ST segment elevation in two leads is
suggestive of an acute myocardial suggestive of an acute myocardial infarction. In this module we will cover:
–
ST Elevation and non-ST Elevation MIs
–
Left Ventricular Hypertrophy
–
Bundle Branch Blocks

101. ST Elevation and
non-ST Elevation MIs

102. ST Elevation and non-ST Elevation MIs •
When myocardial blood supply is abruptly
reduced or cut off to a region of the heart, a
sequence of injurious events occur beginning
with
ischemia
(inadequate tissue perfusion),
followed by
necrosis
(infarction), and eventual
followed by
necrosis
(infarction), and eventual
fibrosis
(scarring) if the blood supply isn't
restored in an appropriate period of time.
•
The ECG changes over time with each of
these events…

103. ECG Changes
Ways the ECG can change include:
ST elevation &
depression
Appearance
of pathologic
Q-waves
T-waves
peaked flattened inverted

104. ECG Changes & the Evolving MI
There are two
distinct patterns
of ECG change
Non-ST
Elevation
of ECG change depending if the
infarction is:
–
ST Elevation
(Transmural or Q-wave),
or
–
Non-ST Elevation
(Subendocardial or non-Q-wave)
ST
Elevation

105. ST Elevation Infarction
The ECG changes seen with a ST elevation infarction are:
Before injury
Normal ECG ST depression, peaked T-waves,
then T-wave inversion
ST elevation
& appearance of
Q-waves
ST segments and T-waves return to
normal, but Q-waves persist
Ischemia Infarction Fibrosis

106. ST Elevation Infarction
Here’s a diagram depicting an evolving infarction:
A.
Normal
ECG prior to MI
B.
Ischemia
from coronary artery occlusion
results in ST depression (not shown) and results in ST depression (not shown) and peaked T-waves
C.
Infarction
from ongoing ischemia results in
marked ST elevation
D/E.
Ongoing infarction
with appearance of
pathologic Q-waves and T-wave inversion
F.
Fibrosis
(months later) with persistent Q-
waves, but normal ST segment and T-
waves

107. ST Elevation Infarction
Here’s an ECG of an
inferior
MI:
Look at the
inferior leads
(II, III, aVF).
Question: What ECG
changes do
you see?
ST elevation
and Q-waves
Extra credit: What is the
rhythm?
Atrial fibrillation (irregularly irregular with nar row QRS)!

108. Non-ST Elevation Infarction
Here’s an ECG of an
inferior
MI later in time:
Now what do
you see in the
inferior leads?
ST elevation,
Q-waves and
T-wave
inversion

109. Non-ST Elevation Infarction
The ECG changes seen with a non-ST elevation infarction are:
Before injury
Normal ECG ST depression & T-wave inversion
ST depression & T-wave inversion
ST returns to baseline, but T-wave
inversion persists
Ischemia Infarction Fibrosis

110. Non-ST Elevation Infarction
Here’s an ECG of an evolving non-ST elevation MI:
Note the ST
depression
and T-wave
inversion in inversion in leads V
2
-V
6
.
Question: What area of
the heart is
infarcting?
Anterolateral

111. Left Ventricular
Hypertrophy

112. Left Ventricular Hypertrophy
Compare these two 12-lead ECGs. What stands
out as different with the second one?
Normal Left Ventricular Hypertrophy
Answer:
The QRS complexes are very tall
(increased voltage)

113. Left Ventricular Hypertrophy
Why is left ventricular hypertrophy characterized by tall
QRS complexes?
As the heart muscle wall thickens there is an increa se in
electrical forces moving through the myocardium res ulting
in increased QRS voltage.
LVH
ECHOcardiogram
Increased QRS voltage
in increased QRS voltage.

114. Left Ventricular Hypertrophy
•
Criteria exists to diagnose LVH using a 12-lead ECG.
–
For example:
•
The R wave in V5 or V6 plus the S wave in V1 or V2
exceeds 35 mm.
•
However, for now, all
you need to know is
that the QRS voltage
increases with LVH.

115. Bundle Branch Blocks

116. Bundle Branch Blocks
Turning our attention to bundle branch blocks…
Remember normal impulse conduction is impulse conduction is
SA node
AV node
Bundle of His
Bundle Branches

Purkinje fibers

117. Normal Impulse Conduction
Sinoatrial node
AV node
Bundle of His
Bundle Branches
Purkinje fibers

118. Bundle Branch Blocks
So, depolarization of
the Bundle Branches
and Purkinje fibers are
seen as the QRS
complex on the ECG. complex on the ECG. Therefore, a conduction
block of the Bundle
Branches would be
reflected as a change in
the QRS complex.
Right
BBB

119. Bundle Branch Blocks
With Bundle Branch Blocks you will see two changes
on the ECG.
1.
QRS complex widens
(> 0.12 sec)
.
2.
QRS morphology changes
(varies depending on ECG lead,
and if it is a right vs. left bundle branch block)
.
and if it is a right vs. left bundle branch block)
.

120. Bundle Branch Blocks
Why does the QRS complex widen? When the conduction pathway is blocked it pathway is blocked it will take longer for
the electrical signal
to pass throughout
the ventricles.

121. Right Bundle Branch Blocks
What QRS morphology is characteristic? For
RBBB
the wide QRS complex assumes a
unique, virtually diagnostic shape in those
leads overlying the right ventricle (V
and V
).
V
1
leads overlying the right ventricle (V
1
and V
2
).
“Rabbit Ears”

122. Left Bundle Branch Blocks
What QRS morphology is characteristic? For
LBBB
the wide QRS complex assumes a
characteristic change in shape in those leads
opposite
the left ventricle (right ventricular
opposite
the left ventricle (right ventricular
leads -V
1
and V
2
).
Broad,
deep S
waves
Normal

123. Summary
This Module introduced you to:
–
ST Elevation and Non-ST Elevation MIs
–
Left Ventricular Hypertrophy
–
Bundle Branch Blocks
–
Bundle Branch Blocks
Don’t worry too much right now about trying to
remember all the details. You’ll focus more on
advanced ECG interpretation in your clinical
years!

124. ECG Filtering

125. Contents
•
Very brief introduction to ECG
•
Some common ECG Filtering tasks
–
Baseline wander filtering
–
Baseline wander filtering
–
Power line interference filtering
–
Muscle noise filtering
•
Summary

126. A Very brief introduction
•
To quote the book:
”
Here a general prelude to ECG signal
”
Here a general prelude to ECG signal
processing and the content of this chapter
(3-5 pages) will be included.”
•
Very nice, but let’s take a little more
detail for those of us not quite so
familiar with the subject...

127. A Brief introduction to ECG
•
The electrocardiogram (ECG) is a time-varying signal
reflecting the ionic current flow which causes the
cardiac fibers to contract and subsequently relax. The
surface ECG is obtained by recording the potential
difference between two electrodes placed on the difference between two electrodes placed on the surface of the skin. A single normal cycle of the ECG
represents the successive atrial
depolarisation/repolarisation and ventricular
depolarisation/repolarisation which occurs with every
heart beat.
•
Simply put, the ECG (EKG) is a device that measures
and records the electrical activity of the heart from
electrodes placed on the skin in specific locations

128. What the ECG is used for?
•
Screening test for coronary artery disease,
cardiomyopathies, left ventricular hypertrophy •
Preoperatively to rule out coronary artery disease
•
Can provide information in the precence of metabolic
alterations such has hyper/hypo calcemia/kalemia
etc.
•
With known heart disease, monitor progression of the
disease •
Discovery of heart disease; infarction, coronal
insufficiency as well as myocardial, valvular and
cognitial heart disease
•
Evaluation of ryhthm disorders
•
All in all, it is the basic cardiologic test and is widely
applied in patients with suspected or known heart
disease

129. Measuring ECG
•
ECG commonly measured via 12
specifically placed leads

130. Typical ECG
•
A typical ECG period consists of P,Q,R,S,T and
U waves

131. ECG Waves
•
P wave: the sequential
activation
(depolarization) of the
right and left atria
•
QRS comples: right and left ventricular and left ventricular depolarization
•
T wave: ventricular
repolarization •
U wave: origin not
clear, probably
”afterdepolarizations” in
the ventrices

132. ECG Example

133. ECG Filtering
•
Three common noise sources
–
Baseline wander
–
Power line interference
–
Muscle noise
•
When filtering any biomedical signal care should be taken not to alter the desired should be taken not to alter the desired information in any way
•
A major concern is how the QRS complex
influences the output of the filter; to the filter
they often pose a large unwanted impulse
•
Possible distortion caused by the filter should
be carefully quantified

134. Baseline Wander

135. Baseline Wander
•
Baseline wander, or extragenoeous low-
frequency high-bandwidth components, can
be caused by:
–
Perspiration (effects electrode impedance)
–
Respiration
–
Respiration
–
Body movements
•
Can cause problems to analysis, especially
when exmining the low-frequency ST-T
segment
•
Two main approaches used are linear filtering
and polynomial fitting

136. BW –Linear, time-invariant
filtering
•
Basically make a highpass filter to cut of the lower-
frequency components (the baseline wander) •
The cut-off frequency should be selected so as to ECG
signal information remains undistorted while as much as
possible of the baseline wander is removed; hence the
lowest
-
frequency component of the ECG should be
lowest
-
frequency component of the ECG should be
saught.
•
This is generally thought to be definded by the slowest
heart rate. The heart rate can drop to 40 bpm, implyi ng
the lowest frequency to be 0.67 Hz. Again as it is not
percise, a sufficiently lower cutoff frequency of about 0.5
Hz should be used.
•
A filter with linear phase is desirable in order to avo id
phase distortion that can alter various temporal
realtionships in the cardiac cycle

137. •
Linear phase response
can be obtained with finite
impulse response, but the
order needed will easily
grow very high
(approximately 2000, see
book for details)
–
Figure shows leves 400
(dashdot) and 2000
(dashed) and a 5th order (dashed) and a 5th order forward-bacward filter (solid)
•
The complexity can be reduced by for example forward-
backward IIR filtering. This has some drawbacks,
however:
–
not real-time (the backward part...)
–
application becomes increasingly difficult at highe r sampling rates
as poles move closer to the unit circle, resulting in unstability –
hard to extend to time-varying cut-offs (will be di scussed shortly)

138. •
Another way of reducing filter complexity is to
insert zeroes into a FIR impulse response,
resulting in a comb filter that attenuates not only
the desired baseline wander but also multiples
of the original samping rate.
–
It should be noted, that this resulting multi-stopband
filter can severely distort also diagnostic information
in the signal in the signal

139. •
Yet another way of reducing filter
complexity is by first decimating and then
again interpolating the signal
•
Decimation removes the high-frequency
content, and now a lowpass filter can be
used to output an estimate of the baseline
wander
•
The estimate is interpolated back to the
original sampling rate and subtracted from
the original signal

140. BW –Linear, time-variant filtering
•
Baseline wander can also be of higher
frequency, for example in stress tests, and in
such situations using the minimal heart rate for
the base can be inefficeient.
•
By noting how the ECG spectrum shifts in
frequency when heart rate increases, one may
suggest coupling the cut
-
off frequency with the
suggest coupling the cut
-
off frequency with the
prevailing heart rate instead
Schematic Schematic example example of of
Baseline Baseline noise and the noise and the
ECG Spectrum at a ECG Spectrum at a
a) lower heart rate a) lower heart rate
b) higher heart b) higher heart rate rate

141. •
How to represent the
”prevailing heart rate”
–
A simple but useful way is
just to estiamet the length of
the interval between R
peaks, the RR interval
–
Linear interpolation for
interior values
•
Time-varying cut-off frequency should be inversely
proportional to the distance between the RR peaks
–
In practise an upper limit must be set to avoid dis tortion in very
short RR intervals
•
A single prototype filter can be designed and subjected
to simple transformations to yield the other filters

142. BW –Polynomial Fitting
•
One alternative to basline removal is to fit polynomials
to representative points in the ECG
–
Knots selected from a
”silent” segment, often the
best choise is the PQ
interval
–
A polynomial is fitted so
–
A polynomial is fitted so that it passes through
every knot in a smooth
fashion
–
This type of baseline
removal requires the QRS
complexes to have been
identified and the PQ
interval localized

143. •
Higher-order polynomials can provide a more
accurate estimate but at the cost of additional
computational complexity
•
A popular approach is the cubic spline estimation
technique
–
third-order polynomials are fitted to successive sets of
triple knots –
By using the third-order polynomial from the Taylor series and requiring the estimate to pass through the series and requiring the estimate to pass through the knots and estimating the first derivate linearly, a
solution can be found –
Performance is critically dependent on the accuracy of
knot detection, PQ interval detection is difficult in mor e
noisy conditions
•
Polynomial fitting can also adapt to the heart rate
(as the heart rate increases, more knots are
available), but performs poorly when too few
knots are available

144. Baseline Wander Comparsion
a)
Original ECG
b)
time
-
invariant
An comparison of the methods for baseline wander An comparison of the methods for baseline wander
removal at a heart rate of 120 beats per minute removal at a heart rate of 120 beats per minute
b)
time
-
invariant
filtering
c)
heart rate
dependent
filtering
d)
cubic spline
fitting

145. Power Line Interference
•Electromagnetic fields from power lines
can cause 50/60 Hz sinusoidal
interference, possibly accompanied by
some of its harmonics
•
Such noise can cause problems interpreting low
-
amplitude waveforms
interpreting low
-
amplitude waveforms
and spurious waveforms can be
introduced. •
Naturally precautions should be taken to
keep power lines as far as possible or
shield and ground them, but this is not
always possible

146. PLI –Linear Filtering
•
A very simple approach to filtering power line
interference is to create a filter defined by a
comple-conjugated pair of zeros that lie on
the unit circle at the interfering frequency ω
0
–
This notch will of course also attenuate ECG waveforms constituted by frequencies close to
ω
waveforms constituted by frequencies close to
ω
0
–
The filter can be improved by adding a pair of
complex-conjugated poles positioned at the same
angle as the zeros, but at a radius. The radius
then determines the notch bandwith.
–
Another problem presents; this causes increased
transient response time, resulting in a ringing
artifact after the transient

147. Pole Pole--zero diagram for two zero diagram for two
second second--order IIR filters with order IIR filters with
idential locations of zeros, but idential locations of zeros, but
with radiuses of 0.75 and 0.95 with radiuses of 0.75 and 0.95
•
More sophisticated filters can be constructed for, for
example a narrower notch •
However, increased frequency resolution is always
traded for decreased time resolution, meaning that it is
not possible to design a linear time-invariant filter t o
remove the noise without causing ringing

148. PLI –Nonlinear Filtering
•One possibility is to create a nonlinear filter which
buildson the idea of subtracting a sinusoid, generated b y
the filter, from the observed signal x(n)
–
The amplitude of the sinusoid v(n) = sin(ω
0
n)is adapted to the
power line interference of the observed signal thro ugh the use of
e(n) = x(n)
–
v(n)
an error function
e(n) = x(n)
–
v(n)
–
The error function is dependent of the DC level of x(n), but that
can be removed by using for example the first diffe rence :
e’(n) = e(n) – e(n-1)
–
Now depending on the sign of e’(n), the value of v(n)is updated
by a negative or positive increment α,
v*(n) = v(n) + αsgn(e’(n))

149. •
The output signal is obtained by subtracting the
interference estimate from the input,
y(n) = x(n) –v*(n)
•
If αis too small, the filter poorly tracks changes
in the power line interference amplitude.
Conversely, too large a αcauses extra noise
due to the large step alterations due to the large step alterations
Filter convergence:
a) pure sinusoid
b)output of filter
with α=1
c) output of filter
with α=0.2

150. PLI –Comparison of linear and
nonlinear filtering
•
Comparison of
power line
interference
removal: removal:
a)
original signal
b)
scond-order IIR filter
c)
nonlinear filter with
transient
suppression, α= 10
µV

151. PLI –Estimation-Subtraction
•
One can also estimate the amplitude and phase
of the interference from an isoelectric sgment,
and then subtract the estimated segment from
the entire cycle
–
Bandpass filtering around the interference can be used
–
The location of the segment
–
The location of the segment can be defined, for example, by
the PQ interval, or with some
other detection criteria. If the
interval is selected poorly, for
example to include parts of the
P or Q wave, the interference
might be overestimated and
actually cause an increase in
the interference

152. •
The sinusoid fitting can be solved by minimizing the
mean square error between the observed signal and the
sinusoid model
–
As the fitting interval
grows, the stopband
becomes increasingly
narrow and passband
increasingly flat,
however at the cost of
the increasing
•
The estimation-subtraction technique can also work
adaptively by computing the fitting weights for example
using a LMS algorithm and a reference input (possibly
from wall outlet)
–
Weights modified for each time instant to minimize MSE between
power line frequency and the observed signal
the increasing oscillatory
phenomenon (Gibbs
phenomenon)

153. Muscle Noise Filtering
•Muscle noise can cause severe problems as
low-amplitude waveforms can be obstructed
–
Especially in recordings during exercise
•
Muscle noise is not associated with narrow band filtering, but is more difficult since the spectral content of the noise considerably band filtering, but is more difficult since the spectral content of the noise considerably overlaps with that of the PQRST complex
•
However, ECG is a repetitive signal and thus
techniques like ensemle averaging can be
used
–
Successful reduction is restricted to one QRS
morphology at a time and requires several beats
to become available

154. MN –Time-varying lowpass
filtering
•
A time-varying lowpass filter with variable
frequency response, for example Gaussian
impulse response, may be used.
–
Here a width function β(n)defined the width of the gaussian,
2
gaussian,
h(k,n) ~ e
-β(n)k
2
–
The width function is designed to reflect local
signal properties such that the smooth segments
of the ECG are subjected to considerable filtering
whereas the steep slopes (QRS) remains
essentially unaltered
–
By making β(n)proportional to derivatives of the
signal slow changes cause small β(n), resulting in
slowly decaying impulse response, and vice versa.

155. MN –Other considerations
•
Also other already mentioned techniques may
be applicable;
–
the time-varying lowpass filter examined with
baseline wander –
the method for power line interference based on
–
the method for power line interference based on trunctated series expansions
•
However, a notable problem is that the
methods tend to create artificial waves, little
or no smoothing in the QRS comples or other
serious distortions
•
Muscle noise filtering remains largely an
unsolved problem

156. Conclusions
•
Both baseline wander and powerline interference
removal are mainly a question of filtering out a
narrow band of lower-than-ECG frequency
interference.
–
The main problems are the resulting artifacts and h ow to optimally remove the noise optimally remove the noise
•
Muscle noise, on the other hand, is more difficult as it
overlaps with actual ECG data •
For the varying noise types (baseline wander and
muscle noise) an adaptive approach seems quite
appropriate, if the detection can be done well. For
power line interference, the nonlinear approach
seems valid as ringing artifacts are almost
unavoidable otherwise

157. The main thing...
The main idea to take home from this section
would, in my opinion be, to always take note
of why you are doing the filtering. The ”best”
way depends on what is most important for way depends on what is most important for the next step of processing –in many cases
preserving the true ECG waveforms can be
more important than obtaining a
mathematically pleasing ”low error” solution.
But then again –doesn’t that apply quite
often anyway?

158. ECG Signal Delineation
And Compression

159. Outline
I.
ECG signal delineation

Definition (What)

Clinical and biophysical background (Why)

Delineation as a signal processing (How)
II.
ECG signal compression

General approach to data compression

ECG signal compression
(Intrabeat/Interbeat/Interlead)
III.
Summary

160. Part I.
EGC signal delineation

161. Delineation -Overview
•Aim –Automatically decide/find onsetsand
offsetsfor every wave (P, QRS, and T) from
ECG signal (PQRST-complex)
•
Note! Experts (Cardiologist) use
manual/visual approach

162. Why?
•
Why –Clinically relevant parameters
such as time intervals between waves,
duration of each wave or composite duration of each wave or composite wave forms, peak amplitudes etc. can
be derived
•
To understand this look how ECG signal
is generated

163. ECG Signal Generation

164. What Are We Measuring?
•
ECG gives (clinical) information from
generation and propagation of electric
signals in the heart.
•
Abnormalities related to generation
(arrhythmia) and propagation (ischemia,
infarct etc.) can be seen in ECG-signal
•
Also localization of abnormality is
possible (12 lead systems and BSM)

165. Clinically Relevant
Parameters
•
ST segment
•QRS duration
Bundle brand block
depolarization
• PR interval
SA ventricles
• QT interval
ventricular
fibrillation
•
ST segment ischemia

166. Signal Processing Approach
to Delineation (How)
•
Clinical importance should now be clear
•
Delineation can also be done manually by experts (cardiologist)

expensive
by experts (cardiologist)

expensive
and time consuming. We want to do
delineation automatically (signal
processing)
•
No analytical solution performance
has to be evaluated with annotated
databases

167. Building Onset/Offset Detector

Many algorithms simulate cardiologist
manual delineation (ground truth)
process:

Experts look 1) where the slope reduce to flat line 2) respect maximum upward, to flat line 2) respect maximum upward, downward slope

Simulate this: define the boundary
according to relative slope reduction
with respect maximum slope LPD
approach

168. Low-Pass Differentiated (LPD)
•
Signal is 1) low-pass filtered i.e. high
frequency noise is removed
(attenuated) and 2) differentiated dv/dt (attenuated) and 2) differentiated dv/dt
•
New signal is proportional to slope
•
Operations can be done using only one
FIR filter :
)(*)( )(nh nx ny
=

169. LPD cont.
•
Each wave has a unique frequency
band thus different low-pass (LP)
filtering (impulse) responses are needed
for each wave (P, QRS, and T) for each wave (P, QRS, and T)
•
Design cut-off frequencies using Power
Spectral Density (PSD) •
Differentiation amplifies (high freq.)
noise and thus LP filtering is required

170. LPD cont..
•
Waves w={P,QRS,T} are segmented
from the i:thheart beat.

+
-
=
=
W
W
n
n
y
e
i
i
ˆ
,...,
ˆ
)
(
0
q
q
•
Using initial and final extreme points
thresholds for can be derived



+
-
=
=
oteherwise
W
W
n
n
y
yw
e
i
i
i
, 0
ˆ
,...,
ˆ
)
(
0
q
q
wK wy w
wK wy w
e
i
e
i
e
o
i
o
i
o
/
/
=
=
h
h

171. LPD cont...
•
Constants are control the boundary detection
they can be learnt from annotated database •
Search backwards from initial extreme point.
•
Search backwards from initial extreme point. When threshold is crossed onset has been
detected •
Search forward from last extreme point and
when threshold is crossed offset is
detected.

172. Part II.
EGC signal compression

173. General Data Compression
•
The idea is represent the
signal/information with fewer bits •
Any signal that contains some
•
Any signal that contains some redundancy can be compressed
•
Types of compression: lossless and
lossy compression •
In lossy compression preserve those
features which carry (clinical)
information

174. ECG Data Compression
1)
Amount of data is increasing:
databases, number of ECG leads,
sampling rate, amplitude resolution sampling rate, amplitude resolution etc.
2)
ECG signal transmission
3)
Telemetry

175. ECG Data Compression
•
Redundancy in ECG data: 1) Intrabeat
2) Interbeat, and 3) Interlead •
Sampling rate, number of bits, signal
•
Sampling rate, number of bits, signal bandwidth, noise level and number of
leads influence the outcome of
compression
•
Waveforms are clinically important
(preserve them) whereas isoelectric
segments are not (so) relevant

176. IntrabeatLossless
Compression
•
Not efficient –has mainly historical
value
•
Sample is predicted as a linear combination of past samples and only combination of past samples and only prediction error is stored (smaller
magnitude):
)(ˆ)(
) ( ... )1 ( )(ˆ
1
nx nx e
pnxa nxa nx
p p
p p
- =
-
+
+
-
=

177. IntrabeatLossyCompression
Direct Method
•
Basic idea: Subsample the signal using
parse sampling for flat segments and
dense sampling for waves:
(
n,x
(n)), n=0,...,N
-
1

(
nk,x
(
nk
)),
(
n,x
(n)), n=0,...,N
-
1

(
nk,x
(
nk
)),
k=0,...,K-1

178. Example AZTEC
•
Last sampled time point is in n0
•
Increment time (n) As long as signal in within certain amplitude limits (flat) within certain amplitude limits (flat)
)) ( )( (
21
)(
)( )(
)}(, ),1 (), ( max{ )(
)}(, ),1 (), ( min{ )(
max min
min max
0 0 max
0 0 min
k k k
n x n x ny
n x n x
nx nx nx n x
nx nx nx n x
+ =
< -
+ =
+
=
e
K
K

179. Intrabeat Lossy Compression
Transform Based Methods
•
Signal is represented as an expansion
of basis functions:
∑
=
=
N
k
k k
w x
1
j
•
Only coefficients need to be restored
•
Requirement: Partition of signal is
needed (QRS-detectors)
•
Method provides noise reduction
∑
=
k
1

180. Interbeat Lossy Compression
•
Heart beats are almost identical
(requires QRS detection, fiducial point)
•
Subtract average beat and code
•
Subtract average beat and code residuals (linear prediction or transform)
1 ,..., 0 )(ˆ)(
)
ˆ
(
1
)(ˆ
1 ,..., 0 )
ˆ
( )(
1
- = - =
+ =
- = + =
-
=
∑
N n ns nx y
nx
L
ns
N n nX nx
i i i
ji
L
j
i
i i
q
q

181. Interlead Compression
•
Multilead (e.g. 12-lead) systems
measure same event from different
angles

redundancy
angles

redundancy
•
Extend direct and transform based
method to multilead environment
–
Extended AZTEC
–
Transform concatenated signals












=
12
2
1
x
x
x
x
M

182. Summary -part I
•
Delineation = automatically detect
waves and their on-and offsets (What) •
Clinically important parameters are
•
Clinically important parameters are obtained (Why)
•
Design algorithm that looks relative
slope reduction (How) •
LPD-method –Differentiate low-pass
filtered signal

183. Summary -part II
•
Compression = remove redundancy:
intrabeat, interbeat, and interlead •
Why
–
Large amount of data,
•
Why
–
Large amount of data,
transmission and telemetry
•
Lossless (historical) and lossy
compression •
Notice which features are lost
(isoelectric segments don’t carry any
clinical information)

184. Summary -part II cont.
•
Intrabeat 1) direct and 2) transform based
methods
–
1) Subsample signal with non-uniform way
–
2) Use basis function (save only weights)
•
Interbeat subtract average beat and code
residuals (linear prediction or transform -
coding)
•
Interlead extend intrabeat methods to
multilead environment

185. QRS Detection

186. QRS Complex
P wave: depolarization of right
and left atrium
QRS complex: right and left
ventricular depolarization
ST-T wave: ventricular
repolarization

187. QRS Detection
•
QRS detection is important in
all kinds of ECG
signalprocessing
•
QRS detector must be able to detect a
large
number of
different QRS
morphologies
number of
different QRS
morphologies
•
QRS detector must
not lock onto certain types
of rhythms
but treat next possible detection as
if it could occur almost anywhere

188. QRS Detection
•
Bandpass characteristics to preserve essential spectral content ( e.g.
enhance QRS, suppress P and T wave), typical center frequency 10 -
25 Hz and bandwidth 5 - 10 Hz
•
Enhance QRS complex from background noise, transform each QRS
complex into single positive peak
•
Test whether a QRS complex is present or not (e.g. a sim ple amplitude
threshold)

189. Signal and Noise Problems
1)
Changes in QRS
morphology
i.
of physiological origin
ii.
due to technical problems
ii.
due to technical problems
2)
Occurrence of noise with
i.
large P or T waves
ii.
myopotentials
iii.
transient artifacts (e.g.
electrode problems)

190. Signal and Noise Problems

191. Estimation Problem
•
Maximum likelihood (ML) estimation
technique to derive detector structure
•
Starting point: same signal model as for
derivation of Woody method for
alignment of evoked responses with
varying latencies

192. QRS Detection
Unknown time of
occurrence q

193. QRS Detection

194. QRS Detection
Unknown time of occurrence and
amplitude a

195. QRS Detection
Unknown time of occurrence, amplitude and
width

196. QRS Detection

197. QRS Detection
Peak-and-valley
picking strategy
•
Use of local extreme values as basis for QRS detect ion
•
Base of several QRS detectors
•
Distance between two extreme values must be within certain
•
Distance between two extreme values must be within certain limits to qualify as a cardiac waveform
•
Also used in data compression of ECG signals

198. Linear Filtering
•
To
enhance QRS
from background noise
•
Examples of linear, time-invariant filters for
QRS detection:
–
Filter that emphasizes segments of signal containing rapid transients (i.e. QRS containing rapid transients (i.e. QRS complexes)
•
Only suitable for resting ECG and good SNR
–
Filter that emphasizes rapid transients +
lowpassfilter

199. Linear Filtering
–
Family of filters, which allow large
variability in signal and noise properties
• Suitable for long-term ECG recordings (because no multipliers)
• Filter matched to a certain waveform not possible in practice
Optimize linear filter parameters (e.g. L
1and L
2)
– Filter with impulse response defined from detected QRS complexes

200. Nonlinear Transformations
•
To produce a single, positive-valued peak for each QRS
complex
–
Smoothed squarer
•
Only large-amplitude events of sufficient
duration (QRS complexes) are preserved in
output signal z(n). –
Envelope techniques
–
Several others

201. Decision Rule
•
To determine
whether or not a QRS complex
has occurred
•
Fixed threshold h
•
Adaptive threshold
–
QRS amplitude and morphology may
change drastically during a course of just a
few seconds
•
Here only amplitude-related decision rules
•
Noise measurements

202. Decision Rule
•
Interval-dependent QRS detection threshold
–
Threshold updated once for every new
detection and is then held fixed during
following interval until threshold is
exceeded and a new detection is found
•
Time
-
dependent QRS detection threshold
•
Time
-
dependent QRS detection threshold
-Improves rejection of large-
amplitude T waves
-Detects low-amplitude
ectopic beats
-Eye-closing period

203. Performance Evaluation
•
Before a QRS detector can be implemented
in a clinical setup
–
Determine suitable parameter values
–
Evaluate the performance for the set of chosen parameters chosen parameters
•
Performance evaluation
–
Calculated
theoretically
or
–
Estimated from database
of ECG
recordings containing large variety of QRS
morphologies and noise types

204. Performance Evaluation
Estimate performance from ECG recordings database

205. Performance Evaluation

206. Performance Evaluation
Receiver operating
characteristics
(ROC)
–
Study behaviour of detector for different detector for different parameter values
–
Choose parameter
with acceptable
trade-off between
P
D
and P
F

207. Summary
•
QRS detection important in all kinds of ECG signal
processing •
Typical structure of QRS detector algorithm:
preprocessing (
linear filter
,
nonlinear transformation
)
and
decision rule
and
decision rule
•
For different purposes (e.g. stress testing or intensive
care monitoring), different kinds of filtering,
transformations and thresholdingare needed
•
Multi-lead
QRS detectors

208. Arrhythmia analysis
(heart rate variability)

209. 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

210. Human nervous
system
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)
Somatic Autonomic
Parasympathetic:
rest
control
Sympathetic:
Fight, fright,
flight

211. Why does heart rate vary?
Why is the variation interesting?
Heart rhythm is due to
the pacemaker cells in
the sinus node
Autonomic nervous system
regulates the sinus node
Analysis of the sinus rhythm provides
information about the state of the
autonomic nervous system

212. 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
•
NN
-
intervals = RR
-
intervals but non
-
normal intervals
excluded
RR-interval

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

214. 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 segment s
(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:
Intervals:
1
)(
-
- =
k k IT
t t k d
mean int.diff.

215. 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 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

216. Model of the heart rate
Integral 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
)(td
u
E
INTEGRATOR
THRESHOLD

217. 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:
t∫
-
= +
k
k
t
t
R d m m
1
))( (
0
t t
m0 mean heart rate
tk time of QRS-complex
m(t) modulation of heart rate
R threshold

218. 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
•
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).

219. 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.
1
)(
-
- =
k k IT
t t k d
1
1
)(
-
-
=
k k
IIT
t t
k d
* Interval function & inverse
(u=unevenly
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
) () ( )(
1
1k
K
k
k k
u
IT
t t tt t d- - =
∑
=
-
d

220. 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
∑
=
- =
K
k
k E
tt td
0
) ( )(
d
∑
∫
-
=
-
=
K
k
E
LE
t
t
h
d
d
t
h
t
d
)
(
)
(
)
(
)
(
t
t
t
•
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)
∑
∫
=
-
=
-
=
k
k
E
LE
t
t
h
d
d
t
h
t
d
0
)
(
)
(
)
(
)
(
t
t
t

221. 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
k
k k
u
HT
tt t kT t d
0
0
) () ( )(
d
k = # of heart beat T 0= average RR-interval length
-
dHTisthe 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 d HT and m(t), one
can see that the spectrum of d HT and m(t) are related,
and spectrum of m(t) can be calculated from the
spectrum of dHT.

222. Representation of the HR…
Performance of the representations
•
Best method to
predict m(t) of IPFM-
model is to use heart
timing representation
(which is based on
this model…) this model…)
•
However: heart timing
representation does
not fully explain the
heart rate variability of
humans
→the IPFM-model
might not be accurate

223. Spectral methods
Which kind of information is gained?
Oscillation in heart rate is related to for
example:
-
body temperature changes 0.05 Hz (once in 20 seconds)
New topic: what kind of
modulating signals do we have?
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

224. Spectral methods…
Which 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
•
Parasympathicus increase, high
-
frequency power increase
•
Ratio between two spectral power describes autonomic
balance

225. Spectral methods…
Problems of spectral analysis
•
Stationarity important
•
Extrabeats violate the stationarity, but they can be removed in the analysis 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: 0.5*mean hr

226. Summary
•
Autonomic nervous system →heart rate varies
•
Measurment of HR →info about autonomic
system •
Analysis methods of HR:
•
Analysis methods of HR:
–
Time domain methods »standard deviations
–
Representations of the heart rate
(intervals, times, heart timing=model based)
–
Model that can predict heart rate: IPFM -model
–
Spectral analysis (to be continued in the next talk)

227. Therapeutic Devices

228. Cardiac Pacemaker
Natural Pacemaker
SA node Primary pacemaker
AV node Secondary pacemaker
Every portion of heart can act as pacemaker, though with less periodic
and less magnitude pulse
Rhythmicity is provided by SA node. Rhythm (HR) influenced by Rhythmicity is provided by SA node. Rhythm (HR) influenced by
Temperature
Chemical activities
Nervous activities

229. Natural Pacemaker
HR increase
Force of ventricular contraction
blood pressure
cardiac output
increased parasympathetic activities
fall in HR
Excitability of Heart: Nature of Electrical Stimulus Excitability of Heart: Nature of Electrical Stimulus
abrupt onset
intense enough
adequate duration

230. Artificial Pacemaker
Electrical stimulator that produces repetitive puls es of current designed to
elicit contractions in atria and/or ventricles (con trolled oscillator)
Consider a strip of muscle, can be taken as a paral lel RC section. If the voltage across is
v and the total current is i, then with a voltage of ∆vin dduration
(
)
R C
Rv
dtdv
C i ii+ = + =
τ= RC∆membrane time constant
(
)
( )
t
t
t
/
/
/
1
1
1
d
d
t
e
b
i
e iR v
e iR v
R
dt
-
-
-
-
=
- =D
- =
when d = ∞, i = ∆v/R = b
b ∆Rheobasic current

231. Pacemaker mode of operation
Two chambers: atrial, ventricular
Three modes
Fixed rate pacemaker: asynchronous/ free running / non triggered / perm anent
Delivers rhythmic stimuli to ventricle at a constan t rate (fixed or externally
controlled by program)
Independent of the natural pacemaker activity
Applied in complete AV block
Problems Problems
Competitive pacing
Ventricular fibrillation
Reduced battery life
Triggered Pacemaker: Responsive to cardiac activity. Two types
Atrial Triggered Pacemaker
P wave is detected
delay of about 0.15 sec (AV conduction time) is giv en
stimulus is delivered to ventricles

232. Pacemaker mode of operation (contd)
Ventricular triggered Pacemaker: sense R wave, avoid competitive pacing
Ventricular Synchronous Pacemaker: delivers stimuli in the
refractory period of ventricles
Ventricular Inhibited Pacemaker: delivers stimuli after a delay of
0.8-1 sec and then waits for another R wave
Pacemaker Energy Sources Pacemaker Energy Sources
Hg-Zn Battery
Hg anode (compressed mixture of HgO, graphite & AgO)
Zn cathode, pores zinc

233. Defibrillator
Device that delivers electric shock to cardiac muscle undergoing fatal
arrhythmia, used to treat ventricular fibrillation
Before 1960, ac defibrillators (5-6 A at 60 Hz for 0.25 -1 sec) were
used
Successive attempts required
Can’t correct atrial fibrillation (turns VF) Can’t correct atrial fibrillation (turns VF)
DC defibrillators has mostly discharging currents forms as:
Lown waveform (20A, 3-6 kV, 10 ms (5+5))
Monopulse waveform (20A, 3-6 kV, 10 ms)
Tapered delay waveform (20A, 1.2 kV, 15 ms)
Trapezoidal waveform (20A, 0.8 kV, 20 ms)

234. Defibrillators
Lown: Capacitor is charged to 100 -400 J
Monopulse: L is replaced by high R
Tapered delay: cascading 2 LC sections
Trapezoidal: wave shaping
Control Circuit
Electrodes (Pads)
6-8 cm dia for adults, 4-6 cm for childs
Anterior-anterior
Anterior-posterior (larger dia)