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Critical input data channels selection for progressive work exercise test by neural network sensitivity analysis

We aimed at training a neural network to classify stress test exercise data into one of three classes: normal, heart failure, or lung failure. Good classification accuracy was obtained using a backpropagation neural network architecture with one hidden layer during cross validation on a data set of 110 vectors, when all 17 channels were used. We further aimed at determining which of these channels were critical to the decision making process. This was done through an input sensitivity analysis. Results showed that nine channels formed a critical superset of which possibly any eight could achieve almost perfect classification. We thus show that faster and more accurate classification may be obtained by input channel elimination due to dimension reduction of input space, which makes better generalization.