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Individualized arrhythmia detection with ECG signals from wearable devices

Version 2 2024-06-05, 11:48
Version 1 2015-04-28, 16:21
conference contribution
posted on 2024-06-05, 11:48 authored by B Nguyen, W Lou, Terry CaelliTerry Caelli, Svetha VenkateshSvetha Venkatesh, D Phung
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.

History

Pagination

570-576

Location

Shanghai, China

Start date

2014-10-30

End date

2014-11-01

ISBN-13

9781479969913

Language

eng

Publication classification

E1 Full written paper - refereed, E Conference publication

Copyright notice

2014, IEEE

Title of proceedings

DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Event

Data Science and Advanced Analytics. Conference (2014 : Shanghai, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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