Leveraging multi-modal user-labeled data for improved accuracy in interpretation of ECG recordings

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Abstract

This paper presents our preliminary design of the Reaching the Frail Elderly Patient for Optimizing Diagnosis of Atrial Fibrillation (REAFEL) system that helps to improve accuracy in interpretation of Electrocardiography (ECG) recordings by leveraging multi-modal user-labeled data and other contextual information from mobile devices. We describe the methods to collect and visualize the data, discuss the challenges associated with the project and conclude the paper by outlining future work.

Original languageEnglish
Title of host publicationProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
PublisherAssociation for Computing Machinery
Publication date8 Oct 2018
Pages636-641
ISBN (Electronic)9781450359665
DOIs
Publication statusPublished - 8 Oct 2018
Event2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018
http://ubicomp.org/ubicomp2018/

Conference

Conference2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
CountrySingapore
CitySingapore
Period08/10/201812/10/2018
SponsorAssociation for Computing Machinery
Internet address

Keywords

  • MHealth
  • Patient Reported Outcomes
  • Personal Health Technology
  • User-labeled data

Cite this

Maharjan, R., Bækgaard, P., & Bardram, J. E. (2018). Leveraging multi-modal user-labeled data for improved accuracy in interpretation of ECG recordings. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 636-641). Association for Computing Machinery. https://doi.org/10.1145/3267305.3267548