Computational Intelligence in
Biomedical and Health Care Informatics
HCA 590 (Topics in Health Sciences)
Rohit Kate
Neural Networks: A Sample
Medical Application
1
Reading
• Chapter 9, Text 5: The Application of Neural
Networks in the Classification of the
Electrocardiogram
2
Role of Electrocardiogram (ECG)
• Heart disease is the largest single cause of
premature deaths
• If detected, some causes of heart disease can
be foreseen and prevented through lifestyle
changes
• Clinical techniques may evaluate the status of
the heart
• ECG is one of the most common such a clinical
technique
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ECG
• Simple, inexpensive and non-invasive
• Records the electrical activity of the heart
• Correlates with the fundamental behavior of
the heart
• Wave shapes describe state of the working
muscle masses
• Rate of cardiac cycle provides rhythm
statements
4
Diagnostic Utilities of ECG
• Provides sufficient detail to diagnose a number of
cardiac abnormalities including potentially fatal
ones, for example, Myocardial Infarction, Left
Ventricular Hypertrophy
• Not all cardiac abnormalities can be identified by
ECG
• But in combination with other clinical techniques,
for example, angiography, echocardiography, ECG
can give a more complete picture of the heart
• No standards are currently available for
diagnostic classification using ECG
5
12-Lead ECG
• Six limb leads measure the cardiac activity in
the frontal plane
• Six chest leads measure the cardiac activity in
the horizontal plane
• Together all 12 leads give a three-dimensional
picture of the heart
• There are 12 electrical signal waves
6
Computerized Classification of the
12-Lead ECG
• Input: 12-lead ECG signals
• Output: Assign patient to one of the possible
diagnostic classes
• Computerized classification of ECG is one of
the earliest examples of use of computers in
medicine
• Meant to assist clinicians, not replace them
7
Steps for Computerized
Classification
• One cannot feed an entire waveform to a
classification technique
• This is an instance of time-series classification
problem
• Steps for computerized classification of ECG:
– Beat detection
– Feature extraction
– Possible feature selection
– Classification
8
Beat Detection
• Automatically locate each cardiac cycle in each
of the leads
• Insert reference markers for the beginning and
end of each inter-wave component
• These are used in the feature extraction step
9
Feature Extraction
• Generate feature from inter-wave measurements
of:
– Intervals
– Durations
– Amplitudes
• No standard features have been agreed upon
• Decided mainly based on expert medical
opinions, medical criteria and some trial and
error
• Could be potentially hundreds of features from
each lead
10
Features and Pathologies
• Some ECG deviations from normal indicate
certain pathologies, for example:
– Q-wave location for specific types of Myocardial
Infarction
– Large QRS complexes indicate ventricular
hypertrophy
– R-R intervals tell about heart rate variability
• Computers can be more accurate in detecting
these and relating them to variuos pathologies
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Feature Selection
• Too many features can confuse a classification
method
• Too few features may miss some important
information
• Feature selection is sometimes done to select
the most contributing features
• Generally done by systematically trying
combinations of features and measuring their
impact of a validation part of the training set
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Neural Networks for ECG
Classification
• Each feature is made an input node
• Each of the diagnostic class is made an output
node:
1 implies present
0 implies not present
• Intermediate numbers may tell the degree of the
diagnostic class present
• Multiple diagnosis may be obtained
• Number of hidden nodes, number of hidden
layers, learning rate etc. are determined using the
a validation set
13
Training Data
• The training data consists of ECG features of
several patients and their know diagnostic
classes
• Important to include wide variety of training
examples to ensure generalization of the
trained network
14
Results
• Several studies have applied neural network
techniques for ECG classification
• Results vary based on the particular
classification classes and features used, vary
from 66% to 95% accuracy
• Training multiple neural networks and
combining their results usually improves the
accuracy
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Neural Networks for ECG
Classification
• Generally performed competitive with
physicians
• Neural networks could identify relationships
between ECG features that may not have been
identified by physicians
• Being a statistical method, it is not able to
explain final diagnosis
– Some methods to extract rules from neural
netowrks have been tried
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Homework 3
Due by 2 pm, next class, Tuesday 10/8
Submit .txt, .doc, .pdf, ppt or a scanned image through D2L
Answer each question in 2-3 sentences
1. Both Support Vector Machines and neural
networks learn a separator for classification.
How do they differ in choosing the
separator?
2. How do they differ in their mechanism to go
from a linear to a non-linear separator?
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