Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization

Recognition rates and other parameters of the base classifiers for combining methods using three base classifiers.

<p>Recognition rates and other parameters of the base classifiers for combining methods using three base classifiers.</p

Recognition rates for different combining methods as well as the proposed method with different number of experts.

<p>Recognition rates for different combining methods as well as the proposed method with different number of experts.</p

Comparison of the recognition rates of the proposed method with some popular classifiers in the literature.

<p>Comparison of the recognition rates of the proposed method with some popular classifiers in the literature.</p

waveform of ECG signal: normal beat.

<p>waveform of ECG signal: normal beat.</p

Block diagram of Undecimated Wavelet Transform.

<p>H(z) and Hr(z) are the decomposition and reconstruction high. pass filters. G(z) and Gr(z) are low pass filters. Term d(.,.) denotes the decomposition coefficients and a(., .) denotes the approximation coefficients.</p

ECG signals: (a) Normal Sinus rhythm beats; (b) Premature Venticular contraction beats; (c) other beats (non conducted P-wave and right bundle branch block beats respectively).

<p>ECG signals: (a) Normal Sinus rhythm beats; (b) Premature Venticular contraction beats; (c) other beats (non conducted P-wave and right bundle branch block beats respectively).</p

Recognition rate of the combiner in the Modified Stacked Generalization method with different number of neurons in the hidden layer.

<p>Recognition rate of the combiner in the Modified Stacked Generalization method with different number of neurons in the hidden layer.</p

A visualization of the Undecimated Wavelet Transform coefficients for a typical ECG beat.

<p>A visualization of the Undecimated Wavelet Transform coefficients for a typical ECG beat.</p