Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy

@article{Steinisch2013EarlyDO,
  title={Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy},
  author={Martin Steinisch and Paul R. Torke and Jens Haueisen and Birgit Hailer and Dietrich H. W. Gr{\"o}nemeyer and Peter Van Leeuwen and Silvia Comani},
  journal={Computers in biology and medicine},
  year={2013},
  volume={43 2},
  pages={144-53}
}
We propose an automatic system for the classification of coronary artery disease (CAD) based on entropy measures of MCG recordings. Ten patients with coronary artery narrowing ≥ or ≤ 50% were categorized by a multilayer perceptron (MLP) neural network based on Linear Discriminant Analysis (LDA). Best results were obtained with MCG at rest: 99% sensitivity, 97% specificity, 98% accuracy, 96% and 99% positive and negative predictive values for single heartbeats. At patient level, these results… CONTINUE READING