ecg denoising using nn.pp

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Project Presentation on- A Neural Network Approach to ECG Denoising 1

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Page 1: ECG DENOISING USING NN.pp

Project Presentation on-

A Neural Network Approach to

ECG Denoising

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Page 2: ECG DENOISING USING NN.pp

Contents Introduction to Neural Network & ECG

Electrocardiography

Downsampling

Implementation of Band Pass Filters

Differentiation

Integration

Squaring

Thresholding

QRS Detection

Activation function

Input to Backpropagation algorithm.

Conclusions

References

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Page 3: ECG DENOISING USING NN.pp

Electrocardiography

Electrocardiography (ECG) is the acquisition

of electrical activity of the heart captured

over time by an external electrode attached to

the skin.

Applications of ECG:

o Find the cause of symptoms of heart disease

such as palpitations, arrhythmia,

cardiomyopathy, cardiomyopathy, heart valve

disease, pericarditis.

Objectives of ECG Denoising:

Removal of Noises such as Power line

interference, base line drift due to respiration,

abrupt baseline shift, electromyogram (EMG)

interference and a composite noise made

from other types

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Page 4: ECG DENOISING USING NN.pp

FlowChartECG Signal Read & Plot

Random Noise Addition

Downsampling

Low –Pass Filter

High-Pass Filter

Differentiating Function

Squaring Function

QRS Detection

Thresholding

Integrating Function

Backpropagation algorithm4

Page 5: ECG DENOISING USING NN.pp

ECG Signal Plot

• Electrocardiography

(ECG)is a transthoracic

interpretation of the

electrical activity of the heart

over a period of time.

• Used to measure the rate

and regularity of heartbeats.

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Page 6: ECG DENOISING USING NN.pp

Noise Addition with ECG signal

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Page 7: ECG DENOISING USING NN.pp

Downsampling

• Process of reducing the sampling rate of a signal or the size of the data.

•The downsampling factor (M) is usually an integer or a rational fraction greater than

unity.

•This factor multiplies the sampling time or, equivalently, divides the sampling rate.

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Page 8: ECG DENOISING USING NN.pp

Low Pass Filter

Response Characteristics

•Purely linear phase

response.

•Power line noise is

significantly attenuated.

•Attenuation of the higher

frequency QRS Complex

& or Muscle noise present

would have also been

significantly attenuated.

Implementation of Band-Pass Filters

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Page 9: ECG DENOISING USING NN.pp

High Pass Filter

Response Characteristics:

•This filter also has purely

linear phase response.

• Attenuation of the T wave

due to the high-pass filter.

•This filter optimally passes

the frequencies characteristic

of a QRS complex while

attenuating lower and higher

frequency signals.

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Page 10: ECG DENOISING USING NN.pp

Contrasting difference of Band-Pass Filters:-

Low-pass High-pass

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Page 11: ECG DENOISING USING NN.pp

Differentiating

Function

•Provides information

about the slope of the

QRS complex.

•P and T waves are further

attenuated while the peak-

to-peak signal

corresponding to the QRS

complex is further

enhanced.

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Page 12: ECG DENOISING USING NN.pp

Squaring &Integration Function

Squaring Function:-

Makes all data points in the processed signal positive and amplifies the output of the derivative process nonlinearly.

Integration function :-

Merging of QRS and T complexes or several peaks at the output of the stage depending upon the size of the window.

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Page 13: ECG DENOISING USING NN.pp

Thresholding

• Use of Sets of thresholds

that are just above the

noise peak levels when

signal-to-noise ratio

increases.

• Overall sensitivity of the

detector improves.

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Page 14: ECG DENOISING USING NN.pp

QRS Detection•Beat detection is synonymous

to the detection of QRS

complexes & it provides the

information about presence of a

heartbeat and its occurrence

time.

Importance of design of a

QRS detector-

•Poor detection may propagate

to subsequent processing steps.

•.Beats that remain undetected

constitute a more severe error.

•Ability to follow sudden or

gradual changes in signal.

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Page 15: ECG DENOISING USING NN.pp

Neural Networks• Massively distributed parallel

processor which has a neural

propensity for storing

experimental knowledge and

making it available for use.

• The basic back-propagation algorithm is based on minimizing the error of the network using the derivatives of the error function.

•Input signal propagate through

the network in supervised

manner consisting of two

passes:

i. Forward Pass

ii. Backward Pass15

Page 16: ECG DENOISING USING NN.pp

Feed-forward Networks

Information flow is unidirectional

Data is presented to Input layer

Passed on to Hidden Layer

Passed on to Output layer

Information is distributed

Information processing is parallel

Internal representation (interpretation) of data

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Page 17: ECG DENOISING USING NN.pp

Backpropagation

Back-propagation training algorithm

Backpropagation adjusts the weights of the NN in order to minimize the network total mean squared error.

Network activation

Forward Step

Error propagation

Backward Step

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Page 18: ECG DENOISING USING NN.pp

Weights

The weights in a neural network are the most important factor in determining its function.

Normally, positive weights are considered as excitatory while negative weights are thought of as inhibitory.

Training is the act of presenting the network with some sample data and modifying the weights to better approximate the desired function.

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Activation Function

Applied to the weighted sum of the inputs of a neuron to produce the output.

Majority of NN uses Sigmoid function because

1.Smooth, continuous, and

monotonically increasing.

(derivative is always positive)

2. Bounded range - but never reaches

max or min.f(x) = 1/(1 + exp(-x))

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Calculate Outputs For Each Neuron Based On The Pattern

The output from neuron j for pattern p is Opj where

and

k ranges over the input indices and Wjk is the weight on the connection from input k to neuron j

Feedforward

Inp

uts

Ou

tpu

ts

jnetjpje

netO

1

1)(

k

kjpkbiasj WOWbiasnet *

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Page 21: ECG DENOISING USING NN.pp

Network Error

The error of output neuron k after the activation of the network on the n-thtraining example (x(n), d(n)) is:

ek(n) = dk(n) – yk(n)

The network error is the sum of the squared errors of the output neurons:

The total mean squared error is the average of the network errors of the training examples.

(n)eE(n) 2k

N

1nN

1

AV (n)EE

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Conclusion

ADD UR OWN

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References P. S. Hamilton and W. J. Tompkins. Quantitative investigation of QRS

detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed.Eng, BME-33:1158{1165, 1987.

G. E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines.Technical Report UTML TR 2010003, Dept. of Comp. Sci., University ofToronto, 2010.

G. B. Moody and R. G. Mark. The impact of the MIT-BIH Arrhythmia

Database. IEEE Engineering in Medicine and Biology Magazine, 20(3):45-50,2001.

George B. Moody. The PhysioNet/Computing in Cardiology Challenge2010:Mind the Gap. In Computing in Cardiology 2010, volume 37, Belfast,2010.

R. Rodrigues. Filling in the Gap: a General Method using Neural Networks.InComputers in Cardiology, volume 37, pages 453{456, 2010.

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