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
Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation
Show others and affiliations
2016 (English)In: / [ed] Murray, A, IEEE, 2016, p. 785-788Conference paper, Published paper (Refereed)
Abstract [en]

Wearable sensor technology like textile electrodes provides novel ambulatory health monitoring solutions but most often goes along with low signal quality. Blind Source Separation (BSS) is capable of extracting the Electrocardiogram (ECG) out of heavily distorted multichannel recordings. However, permutation indeterminacy has to be solved, i.e. the automated selection of the desired BSS output. To that end we propose to exploit the sparsity of the ECG modeled as a spike train of successive heartbeats. A binary code derived from a two-item dictionary {peak, no peak} and physiological a-priori information temporally represents every BSS output component. The (best) ECG component is automatically selected based on a modified Hamming distance comparing the components' code with the expected code behavior. Non-standard ECG recordings from ten healthy subjects performing common motions while wearing a sensor garment were subsequently processed in 10 s segments with spatio-temporal BSS. Our sparsity-based selection RCODE achieved 98.1% heart beat detection accuracy (ACC) by selecting a single component each after BSS. Traditional component selection based on higher-order statistics (e.g. skewness) achieved only 67.6% ACC.

Place, publisher, year, edition, pages
IEEE, 2016. p. 785-788
Series
Computing in Cardiology Conference, ISSN 2325-8861 ; 43
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-138248DOI: 10.23919/CIC.2016.7868860ISI: 000405710400197ISBN: 978-1-5090-0895-7 (electronic)OAI: oai:DiVA.org:umu-138248DiVA, id: diva2:1134912
Conference
43rd Computing in Cardiology Conference (CinC), SEP 11-14, 2016, Vancouver, CANADA
Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2018-06-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Wiklund, Urban

Search in DiVA

By author/editor
Wiklund, Urban
By organisation
Radiation Physics
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 60 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