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Paper   IPM / Cognitive Sciences / 12159
School of Cognitive Sciences
  Title:   Improving ECG classification accuracy using an ensemble of neural network modules
1.  Mehrdad Javadi
2.  Reza Ebrahimpour
3.  Atena Sajedin
4.  Soheil Faridi
5.  Shokoufeh Zakernejad
  Status:   Published
  Journal: Plos One
  Vol.:  6
  Year:  2011
  Pages:   1-13
  Supported by:  IPM
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

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