Motion and Noise Artifact-Resilient Atrial Fibrillation Detection Using a Smartphone

Rifat Zaman, Jo Woon Chong, Chae Ho Cho, Nada Esa, David D. McManus, Ki H. Chon

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

4 Scopus citations

Abstract

Smartphone signals corrupted by motion and noise artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application [1]. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values [1]. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 1st International Conference on Connected Health
Subtitle of host publicationApplications, Systems and Engineering Technologies, CHASE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-369
Number of pages4
ISBN (Electronic)9781509009435
DOIs
StatePublished - Aug 16 2016
Event1st IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016 - Washington, United States
Duration: Jun 27 2016Jun 29 2016

Publication series

NameProceedings - 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016

Conference

Conference1st IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016
CountryUnited States
CityWashington
Period06/27/1606/29/16

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  • Cite this

    Zaman, R., Chong, J. W., Cho, C. H., Esa, N., McManus, D. D., & Chon, K. H. (2016). Motion and Noise Artifact-Resilient Atrial Fibrillation Detection Using a Smartphone. In Proceedings - 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016 (pp. 366-369). [7545868] (Proceedings - 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CHASE.2016.75