Journal
DIAGNOSTICS
Volume 11, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics11081446
Keywords
arrhythmia detection; heart rate; RR interval; atrial fibrillation; atrial flutter; deep learning; residual neural network; detrending
Categories
Funding
- Grow MedTech [PoF000099]
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This paper proposes a deep learning algorithm for automated arrhythmia detection with high accuracy. Tested on 4051 subjects, the algorithm achieved an accuracy of 99.98%. This cost-effective method enables early detection of arrhythmia, leading to improved outcomes for patients.
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.
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