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Electrocardiographic deviation detection: Using long short-term memory recurrent neural networks to detect deviations within electrocardiographic records
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Artificial neural networks have been gaining attention in recent years due to theirimpressive ability to map out complex nonlinear relations within data. In this report,an attempt is made to use a Long short-term memory neural network for detectinganomalies within electrocardiographic records. The hypothesis is that if a neuralnetwork is trained on records of normal ECGs to predict future ECG sequences, it isexpected to have trouble predicting abnormalities not previously seen in the trainingdata. Three different LSTM model configurations were trained using records fromthe MIT-BIH Arrhythmia database. Afterwards the models were evaluated for theirability to predict previously unseen normal and anomalous sections. This was doneby measuring the mean squared error of each prediction and the uncertainty of over-lapping predictions. The preliminary results of this study demonstrate that recurrentneural networks with the use of LSTM units are capable of detecting anomalies.

Place, publisher, year, edition, pages
2018. , p. 26
Keywords [en]
ECG, LSTM, RNN, Neural Network, Deeplearning4j, Time Series
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-76411OAI: oai:DiVA.org:lnu-76411DiVA, id: diva2:1223795
External cooperation
HiQ Karlskrona
Subject / course
Computer Science
Educational program
Computer Engineering Programme, 180 credits
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
Permanent link

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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
  • text
  • asciidoc
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