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  4. Towards Identification of Biometric Properties in Blood Flow Sounds Using Neural Networks and Saliency Maps
 
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2022
Journal Article
Title

Towards Identification of Biometric Properties in Blood Flow Sounds Using Neural Networks and Saliency Maps

Abstract
In previous work, we demonstrated the potential of blood flow sounds for biometric authentication acquired by a custom-built auscultation device. For this purpose, we calculated the frequency spectrum for each cardiac cycle represented within the measurements based on continuous wavelet transform. The resulting spectral images were used to train a convolutional neural network based on measurements from seven users. In this work, we investigate which areas of those images are relevant for the network to correctly identify a user. Since they describe the frequencies’ energy within a cardiac cycle, this information can be used to gain knowledge on biometric properties within the signal. Therefore, we calculate the saliency maps for each input image and investigate their mean for each user, opening perspectives for further investigation of the spectral information that was found to be potentially relevant.
Author(s)
Henze, Jasmin  
Fraunhofer-Institut für Software- und Systemtechnik ISST  
Fuentealba, Patricio
Universidad Austral de Chile
Salvi, Rutuja
IDTM GmbH
Sahare, Natasha
Fraunhofer-Institut für Software- und Systemtechnik ISST  
Bisgin, Pinar  
Fraunhofer-Institut für Software- und Systemtechnik ISST  
Burmann, Anja  
Fraunhofer-Institut für Software- und Systemtechnik ISST  
Illanes, Alfredo
Otto von Guericke University of Magdeburg
Friebe, Michael
AGH University of Science and Technology
Journal
Current directions in biomedical engineering  
Conference
Joint Annual Conference of the Austrian, German and Swiss Societies for Biomedical Engineering 2022  
Open Access
DOI
10.1515/cdbme-2022-1138
Additional full text version
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Language
English
Fraunhofer-Institut für Software- und Systemtechnik ISST  
Keyword(s)
  • saliency maps

  • continuous wavelet transform

  • convolutional neural networks

  • biometry

  • blood flow sounds

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