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Computational Intelligence in Biomedical and Health Care Informatics HCA 590 (Topics in Health Sciences). Rohit Kate. Neural Networks: A Sample Medical Application. Reading. Chapter 9, Text 5: The Application of Neural Networks in the Classification of the Electrocardiogram.

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Computational Intelligence in Biomedical and Health Care InformaticsHCA 590 (Topics in Health Sciences)

Rohit Kate

Neural Networks: A Sample Medical Application

reading
Reading
  • Chapter 9, Text 5: The Application of Neural Networks in the Classification of the Electrocardiogram
role of electrocardiogram ecg
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
diagnostic utilities of ecg
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
12 lead ecg
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
computerized classification of the 12 lead ecg
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
steps for computerized classification
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
beat detection
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
feature extraction
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
features and pathologies
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
feature selection
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
neural networks for ecg classification
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
training data
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
results
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
neural networks for ecg classification1
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 3Due by 2 pm, next class, Tuesday 10/8Submit .txt, .doc, .pdf, ppt or a scanned image through D2L

Answer each question in 2-3 sentences

  • Both Support Vector Machines and neural networks learn a separator for classification. How do they differ in choosing the separator?
  • How do they differ in their mechanism to go from a linear to a non-linear separator?