Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Steinisch, M; Torke, PR; Haueisen, J; Hailer, B; Grönemeyer, D; Van, Leeuwen, P; Comani, S.
Early detection of coronary artery disease in patients studied with magnetocardiography: an automatic classification system based on signal entropy.
Comput Biol Med. 2013; 43(2): 144-53.
Doi: 10.1016/j.compbiomed.2012.11.014
Web of Science
PubMed
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FullText_MUG
- Co-authors Med Uni Graz
- Torke Paul Rudi
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- Abstract:
- 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 correspond to a correct classification of all patients. The classifier's suitability to detect CAD-induced changes on the MCG at rest was validated with surrogate data.
- Find related publications in this database (using NLM MeSH Indexing)
- Adult - administration & dosage
- Aged - administration & dosage
- Coronary Artery Disease - classification, diagnosis
- Discriminant Analysis - administration & dosage
- Early Diagnosis - administration & dosage
- Entropy - administration & dosage
- Female - administration & dosage
- Humans - administration & dosage
- Magnetocardiography - methods
- Male - administration & dosage
- Middle Aged - administration & dosage
- Reproducibility of Results - administration & dosage
- Sensitivity and Specificity - administration & dosage
- Signal Processing, Computer-Assisted - administration & dosage
- Find related publications in this database (Keywords)
- Automatic classification system
- Magnetocardiography
- Entropy
- Coronary artery disease
- Early diagnosis