Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy-Reference-Cited by-同舟云学术

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

Published:2013-02 Issue:2 Volume:43 Page:144-153
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Steinisch Martin,Torke Paul R.,Haueisen Jens,Hailer Birgit,Grönemeyer Dietrich,Van Leeuwen Peter,Comani Silvia

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference38 articles.

1. Combined use of exercise electrocardiography, coronary calcium score and cardiac CT angiography for the prediction of major cardiovascular events in patients presenting with stable chest pain;Versteylen;Int. J. Cardiol.,2012

2. Diagnostic value of coronary CT angiography with prospective ECG-gating in the diagnosis of coronary artery disease: a systematic review and meta-analysis;Sun;Int. J. Cardiovasc. Imaging,2012

3. Application of body surface potential mapping in coronary artery disease diagnosis;Sobieszczanska;Anatol. J. Cardiol.,2007

4. Cardiomagnetism;Stroink,2006

5. Passive vortex currents in magneto- and electrocardiography: comparison of magnetic and electric signal strengths;Dutz;Phys. Med. Biol.,2006

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1. The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG);Biomedical Physics & Engineering Express;2024-05-07

2. Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features;Physiological Measurement;2023-12-01

3. Co-registration of OPM-MCG signals with CT using optical scanning;iScience;2023-11

4. Electromagnetic field and cardiovascular diseases: A state‐of‐the‐art review of diagnostic, therapeutic, and predictive values;The FASEB Journal;2023-08-31

5. Classification of Current Density Vector Maps for Heart Failures Using a Transfer Convolutional Neural Network;IEEE Access;2022

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