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Two-stage artificial intelligence clinical decision support system for cardiovascular assessment using convolutional neural networks and decision trees
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (STC)
University of Salerno, Fisciano, Italy .
Premicare AB, Sörberge, Sweden.
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2020 (English)In: BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, SciTePress, 2020, p. 199-205Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an artificial-intelligence–assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Place, publisher, year, edition, pages
SciTePress, 2020. p. 199-205
Keywords [en]
Artificial Intelligence, Cardiovascular Assessment, Decision Trees, Deep Learning, Feature Selection
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:miun:diva-38960DOI: 10.5220/0008941801990205Scopus ID: 2-s2.0-85083591534ISBN: 9789897583988 (print)OAI: oai:DiVA.org:miun-38960DiVA, id: diva2:1427260
Conference
13th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020; Valletta; Malta; 24 February 2020 through 26 February 2020
Available from: 2020-04-29 Created: 2020-04-29 Last updated: 2020-04-29Bibliographically approved

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Pasha, ShahabLundgren, Jan

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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