Successfully reported this slideshow.
Upcoming SlideShare
×

ECG DENOISING USING NN.pp

436 views

Published on

GREATLY MODIFIED BY SPECIAL PROFESSORS

Published in: Engineering, Technology
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

ECG DENOISING USING NN.pp

1. 1. Project Presentation on- A Neural Network Approach to ECG Denoising 1
2. 2. 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 2
3. 3. 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 3
4. 4. FlowChart ECG Signal Read & Plot Random Noise Addition Downsampling Low –Pass Filter High-Pass Filter Differentiating Function Squaring Function QRS Detection Thresholding Integrating Function Backpropagation algorithm 4
5. 5. 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. 5
6. 6. Noise Addition with ECG signal 6
7. 7. 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. 7
8. 8. 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 8
9. 9. 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. 9
10. 10. Contrasting difference of Band-Pass Filters:- Low-pass High-pass 10
11. 11. 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. 11
12. 12. 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. 12
13. 13. 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. 13
14. 14. 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. 14
15. 15. 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 Pass 15
16. 16. 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 16
17. 17. 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 17
18. 18. 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. 18
19. 19. 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)) 19
20. 20. 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 Inputs Outputs jnetjpj e netO    1 1 )(  k kjpkbiasj WOWbiasnet * 20
21. 21. Network Error The error of output neuron k after the activation of the network on the n-th training 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) 2 k   N 1n N 1 AV (n)EE 21
22. 22. Conclusion  ADD UR OWN 22
23. 23. 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 of Toronto, 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.In Computers in Cardiology, volume 37, pages 453{456, 2010. 23
24. 24. 24