umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • text
  • asciidoc
  • rtf
Classification of Heart Sounds with Deep Learning
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Health care is becoming more and more digitalized and examinations of patients from a distance are closer to reality than fiction. One of these examinations would be to automatically classify a patient-recorded audiosegment of its heartbeats as healthy or pathological. This thesis examines how it can be achieved by examining different kinds of neural networks; convolutional neural networks (CNN) and long short-term memory networks (LSTM). The theory of artificial neural networks is explained. With this foundation, the feed forward CNN and the recurrent LSTM-network have their methods described. Before these methods can be used, the required pre-processing has to be completed, which is different for the two types of networks. Using this theory, the process of how to implement the networks in Matlab is explained. Different CNN:s are compared to each other, then the best performing CNN is compared to the LSTM-network. When comparing the two different networks to each other, cross validation is used to achieve the most correct result possible. The networks are compared by accuracy, least amount of training time and least amount of training data. A final resulti s presented, to show which type of network has the best performance, together with a discussion to explain the results. The CNN performed better than the LSTM-network in all aspects. A reflection on what could have been done differently to achieve a better result is posted.

Place, publisher, year, edition, pages
2018. , p. 52
Series
UMNAD ; 1146
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-149699OAI: oai:DiVA.org:umu-149699DiVA, id: diva2:1223890
External cooperation
Västerbotten County Council Biomedical Engineering R&D
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

Open Access in DiVA

fulltext(805 kB)70 downloads
File information
File name FULLTEXT01.pdfFile size 805 kBChecksum SHA-512
bcb1b484430a41b3c99452584ee965b5560ef59b677a6f049a058d13f0e5baecb931541b2dffb9feda6092ff18d657b23cf9131f49d27f2c6aa06eb7be1c7253
Type fulltextMimetype application/pdf

By organisation
Department of Computing Science
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 70 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 146 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • text
  • asciidoc
  • rtf