Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification

Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized beat-to-beat (R–R) intervals and features relative to the width of the ventricular depolarization waves (QRS complex) are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats, respectively. In comparison with other state-of-the-art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.

@Article{SA20,
  author       = {Saenz Cogollo, J. and Agelli, M.},
  title        = {Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification},
  journal      = {Algorithms},
  number       = {4},
  volume       = {13},
  pages        = {75},
  month        = {april},
  year         = {2020},
  publisher    = {MDPI},
  keywords     = {ECG feature selection, heartbeat classification, arrhythmia detection, random forest classifier},
  doi          = {10.3390/a13040075},
  url          = {https://publications.crs4.it/pubdocs/2020/SA20},
}