Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Biosignal Feature Extraction Techniques for IoT Healthcare Platform
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Electronic and embedded systems. KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits. University of Turku, Finland. (iGrid, IoT4Health)ORCID iD: 0000-0003-2357-1108
University of Turku, Finland. (Communication System, IoT4Health)
University of Turku, Finland. (Embedded Electronics, IoT4Health)
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits. University of Turku, Finland.
2016 (Swedish)In: IEEE Conference on Design and Architectures for Signal and Image Processing (DASIP2016), Rennes, France, 2016Conference paper, Oral presentation only (Other (popular science, discussion, etc.))
Abstract [en]

In IoT healthcare platform, a variety of biosignals are acquired from its sensors and appropriate feature extraction techniques are crucial in order to make use of the acquired biosignal data and help the healthcare scientist or bio-engineer to reach at optimal decisions. This work reviews the existing biosignal feature extraction and classification methods for different healthcare applications. Due the enormous amount of different biosignals and since most healthcare applications uses electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), Electrogastrogram (EGG), we focus the review on feature extractions and classification method for these biosignals. The review also includes a summary of Blood Oxygen Saturation determined by Pulse Oximetry (SpO2), Electrooculography and eye movement (EOG), and Respiration (RSP) signals. Its discussion and analysis focuses on advantages, performance and drawbacks of the techniques.

Place, publisher, year, edition, pages
Rennes, France, 2016.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238468OAI: oai:DiVA.org:kth-238468DiVA, id: diva2:1261713
Conference
IEEE Conference on Design and Architectures for Signal and Image Processing (DASIP2016), October 12-14, 2016 Rennes, France
Note

QC 20181114

Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2018-11-14Bibliographically approved

Open Access in DiVA

DASIP2016(204 kB)34 downloads
File information
File name FULLTEXT01.pdfFile size 204 kBChecksum SHA-512
66d5cbe96194f48cb7e2626aa78d4384dc098932785d1fbf2e7c844c50e9614287ee063970b5716775aa2a94768b4a2f2659ca636b85c8295d0039c6e676d4e5
Type fulltextMimetype application/pdf

Authority records BETA

Kelati, AmlesetTenhunen, Hannu

Search in DiVA

By author/editor
Kelati, AmlesetTenhunen, Hannu
By organisation
Electronic and embedded systemsIntegrated devices and circuits
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 34 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 289 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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