Automated Arrhythmia Detection Based on RR Intervals

FAUST, Oliver, KAREEM, Murtadha, ALI, Ali, CIACCIO, Edward J. and ACHARYA, U. Rajendra (2021). Automated Arrhythmia Detection Based on RR Intervals. Diagnostics, 11 (8).

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Official URL: https://www.mdpi.com/2075-4418/11/8/1446
Open Access URL: https://www.mdpi.com/2075-4418/11/8/1446/pdf (Published version)
Link to published version:: https://doi.org/10.3390/diagnostics11081446
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    Abstract

    Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.

    Item Type: Article
    Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2075-4418 **History: published 10-08-2021; accepted 03-08-2021
    Uncontrolled Keywords: arrhythmia detection, heart rate, RR interval, atrial fibrillation, atrial flutter, deep learning, residual neural network, detrending
    Identification Number: https://doi.org/10.3390/diagnostics11081446
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 13 Aug 2021 11:48
    Last Modified: 13 Aug 2021 12:00
    URI: http://shura.shu.ac.uk/id/eprint/28934

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