title: Improving ECG classification accuracy using an ensemble of neural network modules.
abstract: 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.
authors: Islamic Azad University, South Tehran Branch, Tehran, Iran. MJavadi@azad.ac.ir
authors: Mehrdad
authors: M
authors: Javadi
authors: Reza
authors: R
authors: Ebrahimpour
authors: Atena
authors: A
authors: Sajedin
authors: Soheil
authors: S
authors: Faridi
authors: Shokoufeh
authors: S
authors: Zakernejad
mesh_terms: D004562:Electrocardiography / Q000145:classification / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D006801:Humans; D016571:Neural Networks, Computer; D011674:Pulse / Q000145:classification*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity