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

Raju Maharjan; Per Bækgaard; Jakob E. Bardram

doi:10.1145/3267305.3267548

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

Raju Maharjan, Per Bækgaard, Jakob E. Bardram

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 language	English
Title of host publication	Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
Publisher	Association for Computing Machinery
Publication date	8 Oct 2018
Pages	636-641
ISBN (Electronic)	9781450359665
DOIs	https://doi.org/10.1145/3267305.3267548
Publication status	Published - 8 Oct 2018
Event	2018 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 2018 → 12 Oct 2018 http://ubicomp.org/ubicomp2018/

Conference

Conference	2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
Country/Territory	Singapore
City	Singapore
Period	08/10/2018 → 12/10/2018
Sponsor	Association for Computing Machinery
Internet address	http://ubicomp.org/ubicomp2018/

Keywords

MHealth
Patient Reported Outcomes
Personal Health Technology
User-labeled data

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10.1145/3267305.3267548

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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

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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.",

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note = "2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 ; Conference date: 08-10-2018 Through 12-10-2018",

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Maharjan, R, Bækgaard, P & Bardram, JE 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. Association for Computing Machinery, pp. 636-641, 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018, Singapore, Singapore, 08/10/2018. https://doi.org/10.1145/3267305.3267548

Leveraging multi-modal user-labeled data for improved accuracy in interpretation of ECG recordings. / Maharjan, Raju; Bækgaard, Per ; Bardram, Jakob E.
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. Association for Computing Machinery, 2018. p. 636-641.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Maharjan R, Bækgaard P , Bardram JE. 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. Association for Computing Machinery. 2018. p. 636-641 doi: 10.1145/3267305.3267548

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

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Keywords

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