ecg denoising using nn.pp
TRANSCRIPT
Project Presentation on-
A Neural Network Approach to
ECG Denoising
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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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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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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
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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Noise Addition with ECG signal
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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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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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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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Contrasting difference of Band-Pass Filters:-
Low-pass High-pass
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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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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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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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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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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
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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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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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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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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