umu.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
A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.ORCID-id: 0000-0002-1313-0934
2019 (Engelska)Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 34403-34415Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB, where an autonomic function cannot be evaluated. This paper presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using the principles of hyperdimensional computing. Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via the classification of the similarity indices into three groups. The distance-based methods achieved good separation of signals into classes with different degrees of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of the autonomic function is problematic or even impossible, thus increasing the correctness of the conventional DB scores.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 7, s. 34403-34415
Nyckelord [en]
Deep breathing test, deep breathing index, similarity analysis, heart rate variability, hyperdimensional computing
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:umu:diva-158768DOI: 10.1109/ACCESS.2019.2904311ISI: 000463487400001OAI: oai:DiVA.org:umu-158768DiVA, id: diva2:1314286
Tillgänglig från: 2019-05-08 Skapad: 2019-05-08 Senast uppdaterad: 2019-05-08Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Personposter BETA

Wiklund, Urban

Sök vidare i DiVA

Av författaren/redaktören
Wiklund, Urban
Av organisationen
Institutionen för strålningsvetenskaper
I samma tidskrift
IEEE Access
Datorsystem

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 486 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