Neural Networks in Neural Networks in ECG classificationECG classification

Under the guidance of Under the guidance of Prof. P. BhattacharyaProf. P. Bhattacharya

Nishant ChandraNishant Chandra Mrigen NegiMrigen Negi

Meru A Patil Meru A Patil

LayoutLayout

History of Neural networks in medicalHistory of Neural networks in medical Need for accurate processingNeed for accurate processing Applications of ANN in medicalApplications of ANN in medical What is ECG?What is ECG? ANN in classification of Arrhythmias ANN in classification of Arrhythmias

and Ischemiaand Ischemia ConclusionConclusion

History of Neural Networks in History of Neural Networks in MedicalMedical

Pioneering work of neural network Pioneering work of neural network has started since 1943 by McCulloch has started since 1943 by McCulloch and Pitts.and Pitts.

Pattern recognition problem was Pattern recognition problem was introduced by Rosenblatt (1958)introduced by Rosenblatt (1958)

Need for accurate processingNeed for accurate processing One of the major goals of observational One of the major goals of observational

studies in medicine is to identify patterns in studies in medicine is to identify patterns in complex data sets.complex data sets.

Correct classification of heart beats is Correct classification of heart beats is fundamental to ECG monitoring systems such fundamental to ECG monitoring systems such as an intensive care etc.as an intensive care etc.

Computers are used to automate signal Computers are used to automate signal processing.processing.

ANNs can detect patterns and make ANNs can detect patterns and make distinctions between different patterns that distinctions between different patterns that may not be apparent to human analysis.may not be apparent to human analysis.

Applications of ANN in medicalApplications of ANN in medical It has been successfully applied to various It has been successfully applied to various

areas of medicine to solve non-linear areas of medicine to solve non-linear problems.problems.

Applications include prediction of Applications include prediction of diagnosis such as:diagnosis such as:– CancerCancer– the onset of diabetes mellitusthe onset of diabetes mellitus– survival prediction in AIDSsurvival prediction in AIDS– eating disorders etceating disorders etc

Applications in signal processing and Applications in signal processing and interpretation involve ECGs or interpretation involve ECGs or electrocardiogramselectrocardiograms

MotivationMotivation

Cardiovascular Diseases contribute Cardiovascular Diseases contribute 29.3% of total deaths in world.29.3% of total deaths in world.

Online ECG monitoring in ICUs/CCUs.Online ECG monitoring in ICUs/CCUs. Acting Specialist in emergency cases.Acting Specialist in emergency cases. Each component (P,QRS,T waves) Each component (P,QRS,T waves)

has different frequencies.has different frequencies. Each individual is different.Each individual is different. Learning by experience.Learning by experience.

What is Electrocardiogram What is Electrocardiogram (ECG) ?(ECG) ?

ECG is the graphic recording of electric ECG is the graphic recording of electric potentials generated by the heart.potentials generated by the heart.

12 lead ECG 12 lead ECG 3 bipolar limb leads – I, II, III3 bipolar limb leads – I, II, III 3 unipolar augmented limb leads - AVF, AVR, 3 unipolar augmented limb leads - AVF, AVR,

AVLAVL 6 unipolar chest leads – V1 to V6.6 unipolar chest leads – V1 to V6.

Anatomy of Heart and ECG signalAnatomy of Heart and ECG signal

Normal ECG signalConducting System of Heart

Posterior

Anterior

Limb leads orientation with respect to heart

Chest leads orientation with respect to heart

The 12 Views of the Heart

12 Lead Normal ECG

6 Limb leads 6 Chest leads

RR

ECG and diseasesECG and diseases

Some of the diseases diagnosed by Some of the diseases diagnosed by ECG are:ECG are: Myocardial Ischemia/Infarction.Myocardial Ischemia/Infarction. Arrhythmias.Arrhythmias. Hypertrophy and enlargement of heart.Hypertrophy and enlargement of heart. Conduction Blocks.Conduction Blocks. Preexcitation Syndromes.Preexcitation Syndromes. Other cardiac disorders.Other cardiac disorders.

Did you know !!Did you know !!

In heart Transplant Acute heart In heart Transplant Acute heart rejection is more likely to happen rejection is more likely to happen when the heart donor was female when the heart donor was female regardless of recipient sex.regardless of recipient sex.

Every 34 seconds, a person dies from Every 34 seconds, a person dies from Heart Diseases in the United States.Heart Diseases in the United States.

Myocardial IschemiaMyocardial Ischemia

Due to lack of adequate blood flow to Due to lack of adequate blood flow to the myocardium.the myocardium.

Ischemia is reversible.Ischemia is reversible. Changes in ECG:Changes in ECG:

T wave peakingT wave peaking Symmetric T wave inversionSymmetric T wave inversion ST segment elevation ST segment elevation

Different ECG Signals

Normal Signal ST segment elevated signal

ECG with T wave inversion ECG Signal with peak T waves

Myocardial Ischemia cont..Myocardial Ischemia cont..

ArrhythmiasArrhythmias

It refers to any disturbance in the It refers to any disturbance in the rate, regularity, site of origin, or rate, regularity, site of origin, or conduction of cardiac electrical conduction of cardiac electrical impulse.impulse.

Broadly two types:Broadly two types: Tachycardia – Heart Rate beyond 100 Tachycardia – Heart Rate beyond 100

bits/minute.bits/minute. Bradycardia – Heart Rate below 60 Bradycardia – Heart Rate below 60

bits/minute.bits/minute.

Different ECG Signals

Normal ECG Signal

ECG signal of Bradycardia patient

ECG signal of Tachycardia patient

Arrhythmias cont ..

Sensitivity (SE) and Specificity (SP)Sensitivity (SE) and Specificity (SP)

Helps us to explore the relationship Helps us to explore the relationship between a diagnostic test and the (true) between a diagnostic test and the (true) presence or absence of disease.presence or absence of disease.

A test which is very sensitive will rarely A test which is very sensitive will rarely miss people with the disease.miss people with the disease.

A specific test will have few false positive A specific test will have few false positive results - it will rarely misclassify people results - it will rarely misclassify people without the disease as being diseased.without the disease as being diseased.

Classification Rate: Classification Rate: CC = 100×(TP+TN)/(TN+TP+FN+FP)]

Sensitivity (SE) and Specificity (SP) Cont…Sensitivity (SE) and Specificity (SP) Cont…

ApproachApproach

Variable attributes considered to Variable attributes considered to affect the training and generalization affect the training and generalization of the ANNs were identified as of the ANNs were identified as follows:follows:– Number of nodes in the hidden layerNumber of nodes in the hidden layer– Feature Selection method employedFeature Selection method employed– Number of files in training setNumber of files in training set– Size of input feature vectorSize of input feature vector– Number of epochsNumber of epochs

Case StudyCase Study

Feature Extraction:Feature Extraction:

Fourier TransformFourier Transform Principal component analysis (PCA)Principal component analysis (PCA)

– widely used in signal processing, widely used in signal processing, statistics, and neural computing.statistics, and neural computing.

– basic goal is to reduce the dimension of basic goal is to reduce the dimension of the data.the data.

Linear Prediction Coding (LPC)Linear Prediction Coding (LPC)

Fourier TransformFourier Transform

QRS complex is extracted by applying a window of some time duration (say 250 ms).

Each QRS complex is Fourier transformed and then the power spectrum is calculated.

The components generated along with the temporal vectors give the feature vector.

QRS spectra of a normal beatQRS spectra of a normal beat

QRS spectra of a Arrhythmia beatQRS spectra of a Arrhythmia beat

PCAPCA

Step 1: Get some dataStep 1: Get some data Step 2: Subtract the meanStep 2: Subtract the mean Step 3: Calculate the covariance Step 3: Calculate the covariance

matrixmatrix Step 4: Calculate the eigenvectors Step 4: Calculate the eigenvectors

and eigenvalues of the covariance and eigenvalues of the covariance matrixmatrix

Step 5: Choosing components and Step 5: Choosing components and forming a feature vectorforming a feature vector

Step 6: Deriving the new data setStep 6: Deriving the new data set

Linear Prediction Coding (LPC)Linear Prediction Coding (LPC)

The basic idea of this technique is that sampled QRS segment can be approximated as a linear combination of the past QRS samples.

a is the i th linear prediction coefficient, and p is the order of the predictor.

LPC coefficients can be extracted using various methods viz Burg’s Method.

Training the NNTraining the NN

Number of neurons in the input layer is determined by the number of elements in the input feature vector.

The output layer is determined by the number of classes desired.

The number of neurons in the hidden layer varies according to the specific recognition task and is determined by the complexity and amount of training data available.

Neural network classifier architecture

Performance AnalysisPerformance Analysis

The performance of the neural classifiers is evaluated by computing the percentages of:– sensitivity (SE), – specificity (SP) and – correct classification (CC)

ResultsResults

Neural Neural ClassifierClassifier

Input LayerInput Layer Hidden LayerHidden Layer

11 1212 55

22 10 3

33 5 2

Results Cont.Results Cont.

Neural Neural ClassifierClassifier

(Avrg.)(Avrg.)

Correct Correct classificationclassification

%%

SensitivitySensitivity

%%SpecificitySpecificity

%%

11 94.8394.83 86.6386.63 94.4294.42

22 91.34 81.33 91.92

33 88.25 76.17 88.95

Results Cont.Results Cont.

How does ANN based classification How does ANN based classification compare with:compare with:– Other ECG widely used interpretation Other ECG widely used interpretation

program?program?Neural networks were 15.5% more sensitiveNeural networks were 15.5% more sensitive

– Expert cardiologistExpert cardiologist10.5% more sensitive than the cardiologist10.5% more sensitive than the cardiologist

ConclusionConclusion

Performance of the neural network strategy has shown higher performance than other classical methods (Cox regression models) in predicting clinical outcomes of the risk of coronary artery disease.

ReferencesReferences [1] M. A. Chikh, F. Bereksi Reguig. Application of

artificial neural networks to identify the premature ventricular contraction (PVC) beats,2004

[2] Costas Papaloukasa, Dimitrios I. Fotiadisb, Aristidis Likasb, Lampros K. Michalis. An ischemia detection method based on artificial neural networks,2002

[3] C.D. Nugent, J.A.C. Webb, N.D. Black, G.T.H. Wright, M. McIntyre. An intelligent framework for the classification of the 12-lead ECG, 1999.

Introduction to Neural Networks in Healthcare, Introduction to Neural Networks in Healthcare, Open Clinic, 2002.Open Clinic, 2002.

[4] M.S. Thaler, The Only EKG Book You’ll Ever [4] M.S. Thaler, The Only EKG Book You’ll Ever Need 3Need 3rdrd Edition, Lippincott Williams & Wilkins. Edition, Lippincott Williams & Wilkins.

P.J Mehta, Understanding ECG, 5P.J Mehta, Understanding ECG, 5thth Edition, The Edition, The National Book Depot.National Book Depot.

Believe it or NOT !!Believe it or NOT !! How much blood does your heart pump?How much blood does your heart pump?

– An average heart pumps 2.4 ounces (70 An average heart pumps 2.4 ounces (70 milliliters) per heartbeat. An average heartbeat milliliters) per heartbeat. An average heartbeat is 72 beats per minute. Therefore an average is 72 beats per minute. Therefore an average heart pumps 1.3 gallons (5 Liters) per minute. heart pumps 1.3 gallons (5 Liters) per minute. In other words it pumps 1,900 gallons (7,200 In other words it pumps 1,900 gallons (7,200 Liters) per day, almost 700,000 gallons Liters) per day, almost 700,000 gallons (2,628,000 Liters) per year, or 48 million (2,628,000 Liters) per year, or 48 million gallons (184,086,000 liters) by the time gallons (184,086,000 liters) by the time someone is 70 years old. That's not bad for a someone is 70 years old. That's not bad for a 10 ounce pump!10 ounce pump!

Men suffer heart attacks about 10 years Men suffer heart attacks about 10 years earlier in life than women do.earlier in life than women do.