Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning

Huang, Ying; Lyle, Jane V.; Razak, Anisa Shahira Ab; Nandi, Manasi; Marr, Celia M.; Huang, Christopher L.‐H.; Aston, Philip J.; Jeevaratnam, Kamalan

doi:10.1016/j.cvdhj.2022.02.001

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Previous research has suggested that multiple exercise tests may be needed to detect the presence of arrhythmias, although the exercising rhythm was more consistent than the postexercise rhythm (Navas de Solis et al, 2016). Computer‐based analysis of the ECG in sinus rhythm has also been proposed as a screening tool for paroxysmal AF, based on complexity analysis with two estimators (Alexeenko et al, 2020) or restitution analysis combined with a machine learning algorithm (Huang et al, 2022). Although these analyses might be a potential screening tool for paroxysmal AF, the diagnosis cannot be confirmed by these techniques alone.…”

Section: Paroxysmal Atrial Fibrillation In Horsesmentioning

confidence: 99%

Implantable loop recorders for detecting arrhythmia in horses: Research tool or diagnostic technique?

Decloedt

2023

Equine Veterinary Education

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Section: Paroxysmal Atrial Fibrillation In Horsesmentioning

confidence: 99%

Implantable loop recorders for detecting arrhythmia in horses: Research tool or diagnostic technique?

Decloedt

2023

Equine Veterinary Education

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“…Diferent ML algorithms are being used for classifcation and prediction targets. Examples of ML prediction and classifcation algorithms are SVM and boosting algorithms [3]. Decisions can be made using the random forest (RF) method, which averages many nodes.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Cardiovascular Disease Risk Prediction

Dalal

Goel

Onyema

et al. 2023

Computational Intelligence and Neuroscience

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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.

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A periodic split attractor reconstruction method facilitates cardiovascular signal diagnoses and obstructive sleep apnea syndrome monitoring

Zhang,

Hirose,

Yamada

et al. 2024

Heliyon

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Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning

Cited by 8 publications

References 42 publications

Implantable loop recorders for detecting arrhythmia in horses: Research tool or diagnostic technique?

Implantable loop recorders for detecting arrhythmia in horses: Research tool or diagnostic technique?

Application of Machine Learning for Cardiovascular Disease Risk Prediction

A periodic split attractor reconstruction method facilitates cardiovascular signal diagnoses and obstructive sleep apnea syndrome monitoring

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