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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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 |
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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: | https://shura.shu.ac.uk/id/eprint/28934 |
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