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Biosignal Feature Extraction Techniques for IoT Healthcare Platform
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, Electronic and embedded systems. KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, 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), Electrical Engineering, Electronics and Embedded systems, 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: 2019-11-25Bibliographically approved

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Kelati, AmlesetTenhunen, Hannu

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