دورية أكاديمية
Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning
| العنوان: | Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning |
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| المؤلفون: | Ying H. Huang, MMath, PhD, Jane V. Lyle, BSc, Anisa Shahira Ab Razak, BA, Manasi Nandi, BSc, PhD, Celia M. Marr, BVMS, MVM, PhD, DEIM, DipECEIM, FRCVS, Christopher L.-H. Huang, BA, BM, BCh, PhD, DM, MD, DSc, ScD, FRSB, Philip J. Aston, BSc, PhD, Kamalan Jeevaratnam, DAHP, DVM, MMedSC, PhD, FRCVS |
| المصدر: | Cardiovascular Digital Health Journal, Vol 3, Iss 2, Pp 96-106 (2022) |
| بيانات النشر: | Elsevier, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Diseases of the circulatory (Cardiovascular) system LCC:Medical technology |
| مصطلحات موضوعية: | Paroxysmal atrial fibrillation, Symmetric Projection Attractor Reconstruction, Equine ECG signals, Normal sinus rhythm, Diagnostic, Machine learning, Diseases of the circulatory (Cardiovascular) system, RC666-701, Medical technology, R855-855.5 |
| الوصف: | Background: Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes. Objective: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machine learning classification. Methods: We obtained ECG signals from 139 active equine athletes (120 control, 19 with a PAF diagnosis). The SPAR method was applied to 9 short (20-second) ECG strips for each subject. An optimal SPAR feature set was determined by forward feature selection for input to a machine learning model ensemble of 3 different classifiers (k-nearest neighbors, linear support vector machine, and radial basis function kernel support vector machine). Imbalanced data were handled by upsampling the minority (PAF) class. A final subject classification was made by taking a majority vote over results from the 9 ECG strips. Results: Our final cross-validated classification for a subject gave an accuracy of 89.0%, sensitivity of 94.8%, specificity of 87.1%, and receiver operating characteristic area under the curve of 0.98, taking PAF as the positive class. Conclusion: The SPAR method and machine learning generated a final model with high sensitivity, suggesting that PAF can be discriminated from short equine ECG strips. This preliminary study indicated that SPAR analysis of human ECG could support patient monitoring, risk stratification, and clinical decision-making. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-6936 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666693622000214; https://doaj.org/toc/2666-6936 |
| DOI: | 10.1016/j.cvdhj.2022.02.001 |
| URL الوصول: | https://doaj.org/article/8f50d0217e2343dcae7574277cb04b72 |
| رقم الأكسشن: | edsdoj.8f50d0217e2343dcae7574277cb04b72 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 26666936 |
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| DOI: | 10.1016/j.cvdhj.2022.02.001 |