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

Connectivity searchlight: A novel approach for MRI information mapping using multivariate connectivity

Brain mapping using magnetic resonance imaging (MRI) is traditionally performed using voxel-wise statistical hypothesis testing. Such mass-univariate approach ignores subtle spatial interactions. The searchlight method, in contrast, uses a multivariate predictive model in each local neighborhood in brain space-named the searchlight. The classification performance is then reported at the center of the searchlight to build an information map. We extend the searchlight technique to take into account additional voxels that can be considered as a meaningful network; i.e., we define a criterion of multivariate connectivity to identify voxels that are statistically dependent on those in searchlight. We coin the term "connectivity searchlight" for the extended searchlight. Using simulated data, we empirically show improved performance for brain regions with low signal-to-noise ratio and recovery of underlying network structures that would otherwise remain hidden. The proposed methodology is general and can be applied to both functional and structural data. We also demonstrate promising results on a well-known fMRI dataset where images of different categories are presented

Confusion matrix of the base classifier 2, for the 3 class ECG signal classification.

<p>The produced ECG signal classes are in table rows while the table columns are the classes of the reference ECG signal.</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

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

Standard deviation and number of neurons in the hidden layer of the best topologies for Stacked Generalization method and Modified Stacked Generalization method.

<p>Standard deviation and number of neurons in the hidden layer of the best topologies for Stacked Generalization method and Modified Stacked Generalization method.</p

Block diagram of Combined Neural Networks; Modified Stacked Generalization method.

<p>Block diagram of Combined Neural Networks; Modified Stacked Generalization method.</p

waveform of ECG signal: normal beat.

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