Individualized arrhythmia detection with ECG signals from wearable devices

Thanh-Binh Nguyen, Wei Lou, Terry Caelli, Svetha Venkatesh, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIEEE/ACM DSAA'2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
Subtitle of host publicationOctober 30 - November 1, 2014, Shanghai, China
EditorsLongbing Cao, George Karypis, Irwin King, Wei Wang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages570-576
Number of pages7
ISBN (Electronic)9781479969913, 9781479969920
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Data Science and Advanced Analytics 2014 - Shanghai, China
Duration: 30 Oct 20141 Nov 2014
https://web.archive.org/web/20141026215611/http://datamining.it.uts.edu.au/conferences/dsaa14/

Conference

ConferenceIEEE International Conference on Data Science and Advanced Analytics 2014
Abbreviated titleDSAA 2014
CountryChina
CityShanghai
Period30/10/141/11/14
Internet address

Keywords

  • arrhythmia detection
  • classification
  • ECG
  • wearable devices

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