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Machine Learning assisted system for the resource-constrained atrial fibrillation detection from short single-lead ECG signals
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

An integration of ICT advances into a conventional healthcare system is spreading extensively nowadays. This trend is known as Electronic health or E-Health. E-Health solutions help to achieve the sustainability goal of increasing the expected lifetime while improving the quality of life by providing a constant healthcare monitoring. Cardiovascular diseases are one of the main killers yearly causing approximately 17.7 million deaths worldwide. The focus of this work is on studying the detection of one of the cardiovascular diseases – Atrial Fibrillation (AF) arrhythmia.  This type of arrhythmia has a severe influence on the heart health conditions and could cause congestive heart failure (CHF), stroke, and even increase the risk of death. Therefore, it is important to detect AF as early as possible. In this thesis we focused on studying various machine learning techniques for AF detection using only short single lead Electrocardiography recordings. A web-based solution was built as a final prototype, which first simulates the reception of a recorded signal, conducts the preprocessing, makes a prediction of the AF presence, and visualizes the result. For the AF detection the relatively high accuracy score was achieved comparable to the one of the state-of-the-art. The work was based on the investigation of the proposed architectures and the usage of the database of signals from the 2017 PhysioNet/CinC Challenge. However, an additional constraint was introduced to the original problem formulation, since the idea of a future deployment on the resource-limited devices places the restrictions on the complexity of the computations being performed for achieving the prediction. Therefore, this constraint was considered during the development phase of the project.

Place, publisher, year, edition, pages
2018. , p. 64
Keywords [en]
E-Health, Atrial Fibrillation detection, ECG, Machine Learning, Recursive Feature Elimination, Sustainability
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-71457OAI: oai:DiVA.org:ltu-71457DiVA, id: diva2:1260968
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level (120 credits)
Supervisors
Examiners
Available from: 2018-11-09 Created: 2018-11-05 Last updated: 2018-11-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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  • en-GB
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  • Other locale
More languages
Output format
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