Detail publikace

Neural Network Based Detection of Myocardial Ischemia

Originální název

Neural Network Based Detection of Myocardial Ischemia

Anglický název

Neural Network Based Detection of Myocardial Ischemia

Jazyk

en

Originální abstrakt

Artificial neural networks (ANN) are widely used in many classification problems. In this work ANNs are used in detection of myocardial ischemia. The principal idea is based on using multilayer perceptrons in QRS complex analysis where ischemia signs due electrophysiological changes are expected. ANN learn themselves from examples of ECG patterns of both ischemic and nonischemic subjects. After the training period, ANN works as a pattern signal recognizer in unknown environment.

Anglický abstrakt

Artificial neural networks (ANN) are widely used in many classification problems. In this work ANNs are used in detection of myocardial ischemia. The principal idea is based on using multilayer perceptrons in QRS complex analysis where ischemia signs due electrophysiological changes are expected. ANN learn themselves from examples of ECG patterns of both ischemic and nonischemic subjects. After the training period, ANN works as a pattern signal recognizer in unknown environment.

BibTex


@inproceedings{BUT10515,
  author="Milan {Blaha} and Ivo {Provazník}",
  title="Neural Network Based Detection of Myocardial Ischemia",
  annote="Artificial neural networks (ANN) are widely used in many classification problems. In this work ANNs are used in detection of myocardial ischemia. The principal idea is based on using multilayer perceptrons in QRS complex analysis where ischemia signs due electrophysiological changes are expected. ANN learn themselves from examples of ECG patterns of both ischemic and nonischemic subjects. After the training period, ANN works as a pattern signal recognizer in unknown environment.",
  address="FEKT VUT BRNO",
  booktitle="Proceedings of 9th Conference and Competition STUDENT EEICT 2003",
  chapter="10515",
  institution="FEKT VUT BRNO",
  year="2003",
  month="january",
  pages="17",
  publisher="FEKT VUT BRNO",
  type="conference paper"
}