Identification of atrial fibrillation predictors on an electrocardiogram using a neural network
A. Yu. Maksakova,
S. A. Kim,
M. A. Ashurova,
I. G. Sergeeva,
N. V. Shlyakhtina,
R. Yu. Epifanov,
S. S. Stolyarov https://doi.org/10.15829/1560-4071-2024-5907
Abstract
Atrial fibrillation (AF) is a common rhythm disorder, a life-threatening complication of which is cardioembolic stroke leading to disability and death. This necessitates the search for early predictors of this pathology. P wave and PR interval abnormalities on electrocardiography (ECG) are associated with the AF risk. Neural networks are considered for rapid ECG analysis in routine practice and identifying the risks of AF occurrence and/or relapse. In recent years, advances in joint projects between medicine and artificial intelligence have made significant progress in the use of open ECG databases for deep machine learning of neural networks. These studies have shown that artificial intelligence makes it possible to identify predictors of AF, which will significantly reduce the risk of mortality due to thromboembolism. This paper reviews in detail the results of published studies that highlight the effectiveness of neural networks to improve AF risk assessment.
About the Authors
A. Yu. Maksakova
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
S. A. Kim
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
M. A. Ashurova
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
I. G. Sergeeva
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
N. V. Shlyakhtina
Novosibirsk National Research State University; Consultative and Diagnostic Outpatient Clinic of the State Novosibirsk Regional Clinical Hospital
Russian Federation
Russian Federation
Novosibirsk
R. Yu. Epifanov
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
S. S. Stolyarov
Novosibirsk National Research State University
Russian Federation
Russian Federation
Novosibirsk
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Supplementary files
- The increasing burden of atrial fibrillation in the adult population and, as a result, cardioembolic stroke, necessitates the search for its early predictors.
- P wave indices are indicators of atrial electrical activity, so assessing their changes on surface electrocardiography using neural networks can be effective in identifying predictors of atrial fibrillation.
- Deep learning neural network models have shown great success in health data processing in recent years.
Review
For citations:
Maksakova A.Yu., Kim S.A., Ashurova M.A., Sergeeva I.G., Shlyakhtina N.V., Epifanov R.Yu., Stolyarov S.S. Identification of atrial fibrillation predictors on an electrocardiogram using a neural network. Russian Journal of Cardiology. 2024;29(11S):5907. (In Russ.) https://doi.org/10.15829/1560-4071-2024-5907
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