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Individualized arrhythmia detection with ECG signals from wearable devices

Nguyen, B, Lou,W, Caelli,T, Venkatesh,S and Phung,D 2014, Individualized arrhythmia detection with ECG signals from wearable devices, in DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics, IEEE, Piscataway, N.J., pp. 570-576, doi: 10.1109/DSAA.2014.7058128.

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Title Individualized arrhythmia detection with ECG signals from wearable devices
Author(s) Nguyen, B
Lou,WORCID iD for Lou,W orcid.org/0000-0002-4711-7543
Caelli,T
Venkatesh,SORCID iD for Venkatesh,S orcid.org/0000-0001-8675-6631
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Conference name Data Science and Advanced Analytics. Conference (2014 : Shanghai, China)
Conference location Shanghai, China
Conference dates 2014/10/30 - 2014/11/1
Title of proceedings DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
Editor(s) [Unknown]
Publication date 2014
Conference series Data Science and Advanced Analytics Conference
Start page 570
End page 576
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) arrhythmia detection
classification
ECG
wearable devices
Summary Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices - they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.
ISBN 9781479969913
Language eng
DOI 10.1109/DSAA.2014.7058128
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072753

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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