https://www.mdu.se/

mdu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-4841-2488
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-1940-1747
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-8704-402X
Visa övriga samt affilieringar
2023 (Engelska)Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, s. 923-924Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc. , 2023. s. 923-924
Nyckelord [en]
acceleration photoplethysmography, cardiovascular, fiducial points, hypertension, PPG, Acceleration, Classification (of information), Learning systems, Statistical tests, Support vector machines, Cardiovascular disease, Causes of death, Clinical parameters, Machine-learning, Photoplethysmogram, Photoplethysmography
Nationell ämneskategori
Kardiologi Medicinteknik
Identifikatorer
URN: urn:nbn:se:mdh:diva-63964DOI: 10.1109/CBMS58004.2023.00344ISI: 001037777900162Scopus ID: 2-s2.0-85166469701ISBN: 9798350312249 (tryckt)OAI: oai:DiVA.org:mdh-63964DiVA, id: diva2:1788354
Konferens
36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023
Tillgänglig från: 2023-08-16 Skapad: 2023-08-16 Senast uppdaterad: 2023-10-25Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

Abdullah, SaadAbdelakram, HafidLindén, MariaFolke, MiaKristoffersson, Annica

Sök vidare i DiVA

Av författaren/redaktören
Abdullah, SaadAbdelakram, HafidLindén, MariaFolke, MiaKristoffersson, Annica
Av organisationen
Inbyggda system
KardiologiMedicinteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 64 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf