Automated arrhythmia detection based on rr intervals
Article
Faust, Oliver, Kareem, Murtadha, Ali Ali, Ciaccio, Edward J. and Acharya, U. Rajendra. 2021. "Automated arrhythmia detection based on rr intervals." Diagnostics. 11 (8). https://doi.org/10.3390/diagnostics11081446
Article Title | Automated arrhythmia detection based on rr intervals |
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Article Category | Article |
Authors | Faust, Oliver, Kareem, Murtadha, Ali Ali, Ciaccio, Edward J. and Acharya, U. Rajendra |
Journal Title | Diagnostics |
Journal Citation | 11 (8) |
Article Number | 1446 |
Number of Pages | 18 |
Year | 2021 |
Place of Publication | 2-s2.0-85112756663 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics11081446 |
Web Address (URL) | https://www.mdpi.com/2075-4418/11/8/1446 |
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. |
Keywords | Arrhythmia detection; heart rate; RR interval; atrial fibrillation; atrial flutter; deep learning; detrending; residual neural network |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Sheffield Hallam University, United Kingdom |
Sheffield Teaching Hospitals, United Kingdom | |
Columbia University, United States | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Singapore University of Social Sciences (SUSS), Singapore |
Permalink -
https://research.usq.edu.au/item/z1v8y/automated-arrhythmia-detection-based-on-rr-intervals
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