Active Learning Methods for Electrocardiographic Signal Classification

  title={Active Learning Methods for Electrocardiographic Signal Classification},
  author={Edoardo Pasolli and Farid Melgani},
  journal={IEEE Transactions on Information Technology in Biomedicine},
In this paper, we present three active learning strategies for the classification of electrocardiographic (ECG) signals. Starting from a small and suboptimal training set, these learning strategies select additional beat samples from a large set of unlabeled data. These samples are labeled manually, and then added to the training set. The entire procedure is iterated until the construction of a final training set representative of the considered classification problem. The proposed methods are… CONTINUE READING
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