Outline of the Approach - "Diagnosis of Arrhythmia Using Neural Networks"

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3.1. Outline of the Approach

The experiments were implemented in MATLAB version 6.1.0.450 release 12.1(2001) on an AMD Duron processor 2.66GHz and 256 DDRAM machine. The neural network was implemented using the neural network toolbox. It is assumed that the beat has already been segmented. The signals used in this experiment were originally sampled at 250Hz.

The beat duration in this application is assumed to be 0.8s. This translates to 200 samples.

The 200 samples are then 4-level wavelet transformed with the 'db6' mother wavelet by means of the multilevel decomposition as described in chapter 2. (P de Chazal and R.B.

Reiley, 2000) have shown that the choice of a mother wavelet does not have any significant impact on the classification; hence the 'db6' was chosen because of its popularity. At each level the wavelet coefficients are discarded. Besides loss of some

high frequency information, the discarding of detail coefficients in fact removes some of

the high frequency noise.

Furthermore, [5] have shown that in fact the significant diagnostic information lies in the low frequency end i.e. 0-40Hz. The resulting fourth level decomposition is 13-

approximation coefficients. This approach was chosen because the difficulty with using

the Fourier transform of the original signal is that the signal samples are usually large. It

is also a great deal of difficulty to choose the suitable features from a large number of

Fourier transform coefficients of the original signal.

The data was then divided into traimng and testing data sets. The training set was made up of 90 vectors (30 from each class) and the testing set was made up of 75 vectors (25 from each class)

Figure 16: Outline of the experimental design

3.2. Choosing design parameters

3.2.1. Wavelet and Fourier transform

The (figure17 & figure 18) show the plots of approximation coefficients and FFT coefficients. Figurel7 explains why the neural network performed poorly when it was trained using the approximation coefficients, i.e. the VT and the VFL approximation cannot be easily separated from each other (as mentioned in the abstract). However the plot of FFT shows that their spectral representation separates the two classes.

Therefore the 13-approximation coefficients are subsequently Fourier transformed. The Fourier transformation is implemented through the 16 point Fast Fourier Transform (FFT) algorithm. Basically the wavelet transform was used merely to reduce the number of samples in the signal. The magnitude of the 16-point FFT of the approximation is symmetrical, therefore only half of the coefficients are enough to characterize the arrhythmia.

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(a) VT approximation (b) SVTA approximation

Figure 17: Plots of approximation coefficients.

i 5 10 15

SVTAFFT

IS 1 6 5

0 5 10

(a) VT approximation (b) SVTA approximation (c) VFL approximation Figure 18 : Plots of 16 point FFT.

3.2.2. Neural networks parameters

The preprocessing stages proved to be the most difficult part of this project. A number of techniques were attempted in order to correctly characterize the signals for classification.

Amongst others were the 13-approximation coefficients, the cross-correlation coefficients of each arrhythmia with the normal. The maximum accuracy obtained with these

approaches was 46% and 56% respectively. These will not be discussed any further in

this report.

According to (Hornik, Stinchcombe and White ,1989), as reported by [11] a feedforward neural network with two layers and non-constant and non-decreasing activation functions

at each layer can approximate any piecewise continuous function from a closed bounded

subset of Euclidean N-dimensional space to Euclidean J-dimensional space with any pre-

specified accuracy, provided that a large number of neurons/unit operators are used in the hidden layer. Hence the failure thereof is a direct result of other factors -other than the

feedfoward notion- such factors as fault in the training or correlation in the features set.

Therefore, a single layer MLP was chosen based on the above theorem.

The advantages of MLP are as follows: (C.G. Looney, 1997)

• Learning is independent of the order in which exemplars are presented.

• The architecture can be manipulated for better results.

The disadvantages of MLP are:

• The training may converge to a local minimum(also know as the "boots trap"). In other words the neural network converges to a solution locally rather than globally. This becomes evident when the network performs poorly on unseen

data.

• It is difficult to choose training parameters such as the learning rate and momentum. However, we address this weakness by choosing a learning algorithm that eliminates the need for choosing these parameters.

The figure 19 below, shows the architecture of a single layer MLP. The optimal number of hidden units was found to be 20-units with the Tansig activation function in both the hidden layer and the output layer. The optimal number of hidden units was obtained by network growing as opposed to network pruning [11].

Figure 19: Single hidden layer architecture

The network outputs were encoded as two outputs where l-of-3 encoding was used to ensure that the classes are fairly apart. This also eases the post-processing and recognition

process. The outputs were activated with the logarithmic sigmoid.

The outputs were encoded as:

Ventricular flutter (VFL) =

Ventricular tachycardia (VT) =

Supraventriculartachyarrhythmia (SVTA=

0.9 0

0.9 0.9

These encodings were chosen because firstly the activation functions squash the outputs

in to the interval [1,-1]- However, the values of 1 and -1 cannot be used because then they would require the activation function to touch its asymptotes. This would effectively require the weight of infinite magnitudes (C.G. Looney, 1997). In the post-processing

stages, the outputs are rounded off. This identifies VFL as"0"

, VT as "1.0"

0 _0_

SVTA 1.0 1.0

. The post-processing is carried out merely to ease identification.

and

The scaled conjugate gradient training algorithm was used, mainly due to advantages that were discussed in section 2.2.1.6. (b). The second best algorithm as discussed in 2.2.1.6, the Levernberg-Marquadt, was also tested, but relative to the SCG it resulted in fairly unsatisfactory convergence, therefore its performance is not discussed any further.

Chapter 4

Results and Discussion

4.1. Results

4.2.1. Quantified results

The proposed approach was evaluated by using signals fro the MIT-BIH database where, classification is known with certainty.

The neural network converged satisfactorily over the training and testing set as shown in figure 20. This translated into 100% classification over the training data. The network converged to the desired mean square error in 600 epochs.

10'

100

Training Convergence

Testing Training

:r.i; h;.=^

200 300 400

600 Epochs

500 BOO

Figure 20: Convergence performance of the neural network on Frequency data

The classification outputs were encoded as explained in 3.2(b). The classification results were is summarized in the confusion matrix similar to one used by (Guler & Ubeyli,

2005). The confusion matrix (figure 21)shows for each class the accuracy with which a particular class is correctly assigned and when it is misclassified, the matrix shows which

class it has been wrongly classified as explained in 2.1.1.

VFL VT SVTA VFL 22/25 3/23 0/25 VT 1/25 24/25 0/25

SVTA 0/25 0/25 25/25

Figure 21: Classification for db6 results relayed on the Confusion matrix

The confusion matrix above shows that SVTA was classified with 100% accuracy while

the misclassification occurred between the VT and VFL.

The classification results are quantified as follows:

correct_ classifications _ 71 Overall Classification Accuracy^

Total_ testing_ samples 75 = 94.66% (14)

Specificity for a class = correct _ classifications _ in_ for _ a _ class total _ number _of _ Test _ samples _ for __ that _ class

Table2 : Specificity of the network over different classes

Class Specificity (%)

VFL 96

SVTA 100

VT 88

.(15)

4.2. Discussion of the results.

In our investigation we found that:

• The convergence time of the neural network was fairly short, because of the chosen Scaled conjugate gradient training algorithm.

• The generalization abilities of the network 94.66% over the unseen data, is a fairly competitive result.

• The generalization performance can be improved by fine tuning other parameters such as encoding output. The output encoding affects classification, however there is no known criteria for choosing outputs.

The table 3 below -adopted from (Niwas, Kumari, Sadasivam, 2005)- summarizes the results of similar work carried out in this direction. The purpose of the table is to compare the performance of our approach to existing approaches. We compare on the basis of number of inputs used as well as accuracy in classification. The approach presented here gives fairly competitive results given that some important information was discarded in

the detail coefficients.

Table 3: Comparison of the ECG classifiers

Method No. of arrhythmia types inputs &J^J&acyi%) Proposed metho d(DWT-

DFT)

S inputs

94.66%

Heart beat interval and

Spectral entropy with neural

network t83

10 99.02

Multiresolution analysis [2] 4 25 inputs ^94.00

Fourier transform with Neural network [3]

15 inputs

98.00

Discrete Fourier Transform, with Neural Network [io]

15 inputs

89.40

Chapter 5

Conclusion and Recommendation

5.1. Conclusions

Through attempting automatic diagnosis of heart conditions, this work has introduced a new preprocessing technique for neural network based classification of abnormal heart activities. The preprocessing technique reduced the feature size from raw signal of 200 samples to 8-points on of the DFT. A single hidden layer neural network with 20-hidden units, trained with scaled conjugate gradient back-propagation was then used as a classifier. The classes were encoded using two outputs.

The classifier system was tested over three arrhythmias, namely SVTA, VT and VFL.

The classifier system gave an overall accuracy of 94.66%, with specificity of 100%, 96%

and 88% over the three classes respectively.

The performance of the approach compares fairly well with other classifiers and the system seems fairly competitive. It improves the performance of DFT based feature systems. In short the approach is an invaluable preprocessing technique and as thus it may be applicable to other signals.

5.2. Recommendations

Through discarding the all the detailed coefficients a significant amount of information was lost. In spite of this fairly competitive classification accuracy was achieved.

Therefore it is recommended that further work investigates the effect of retaining the detail coefficients - while maintaining all the other factors - on the classification

accuracy.

Authors such as (Dokur, T.Olmez and E. Yazgan, 1999) reported that the performance of Fourier based classification deteriorates with increasing classes. It would be worthwhile to test the wavelet-Fourier preprocessing for deterioration with increasing number of classes. If the DWT compensates the Fourier transform when the number of classes is increased, this would present a powerful technique, whereby high accuracy can be achieved over a large number of classes by using a small neural network with a few inputs.

The second recommendation is to analyze the power of this preprocessing technique in classifications involving other signals such as images, vibrations (in structural monitoring), speech recognition data and seismic data.

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[2]Gulerand Ubeyli.(2005)

ECG beatclassifierdesignedbycombined neuralnetwork model.

The Journal of pattern recognition society. Elsevier June 2005

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Classification ofArrhythmias using Multi-resolution analysis andNeural Networks. IEEE Trans on Biomed, 2003.

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Comparison ofdiscrete wavelet andFourier transformsfor ECG beat classification. Electronics letters, Vol.35 No. 18,2nd September

1999.

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Real-time Discrimination of Ventricular Tachyarrhythmia with Fourier- Transform and Neural Network. IEEE Biomed Vol. 46 No. 2, February 1999.

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A Patient-Adaptable ECG Beat Classifier using a Mixture ofExperts Approach.

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Comparing Wavelet transformsfor recognizing cardiac patterns. IEEE, Engineering in Medicine and Biology, April 1995.

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A comparison of the use of different wavelet coefficients for classification of Electrocardiogram. IEEE explore.

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Pattern recognition using neural network: theory and algorithms for engineers.

Oxford University Press USA

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Multilayerfeedforward neural networks are universal approximators. Neural Networks, vol 2 pp359-366.

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A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7:115 -133.

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A scaled conjugate gradient algorithm forfast supervised learning. Neural Networks Vol 6. pp 525-533,1993.

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[17] (Phillips, Parr, Riskin, 2003)

Signals, Systems and transform. 3rd Edition. Prentice Hall publishers Upper

Saddle River, NJ

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Afirst course in Wavelet with Fourier Analysis. Prentice Hall Inc. New Jersey

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Introduction to Wavelets and Wavelet transforms: A primer.

Prentice Hall publishers.

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Artificial neural networks based automatic cardiac abnormalities classification.

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Appendix-A

Pattern Recognition methods

Decision-theoretic

• Statistical

- Parametric, non-Parametric methods, Bayesian Estimation.

• Graph theoretic

• Rule based

(i) Binary Logical rules, (ii) Fuzzy logical rules.

• Structural 48

(i) Automata

- Deterministic - Stochastic.

- Hopfield recurrent neural networks - Bidirectional associative maps.

• Associative mappings (Neuro-fuzzy mapping) (ii) Feedforward neural networks.(FF-NN) - Multiple layer perceptron.(MLP)

- Functional link nets.

- Radial Basis function networks,

(iii) Self-organizing neural networks - Self-organizing feature maps.

- Fuzzy c-means clustering algorithms.

- Fuzzy self-organizing maps.

- Adaptive resonance theory.

• Hybrid networks

(i) Learning vector quantization networks, (ii) Probabilistic neural networks,

(iii) Fuzzy Associative maps.

(iv) Fuzzy learning vector quantization networks.

Appendix B

Neural Network toolbox interface

The neural network toolbox GUI is a friendly way to designing neural networks.

Data can be imported and exported betweenthis interface and the MATLAB work

space.

The neural network is created by clicking new network in the network data manager. The create new-network interface appears. In this interface a number of neural network options are available. The design parameters of each network can be specified as indicatedin the interface

This GUI offers an option of only one hidden layer.

;View Kinrtjail&j Simulate | Train, ] ;Adapt$*Weights

—* LJjp.il

Ml

l?-^

b[2}-

Network/Data Manager

nputs: ^Networks: Outputs:

targets: ^Errors:

nput Delay States: Layer Delay States:

Networks and Data-

Help_{i „} 4 ;New Data;,:_a _{•-.'••as.} _•_'- "V?c- ••' I •..^Ne^Network...•'• -

Export... View || Delete

-Networksjnly^

Initialize.^ ^Simulate... Train... Adapt:;:

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