Comparison of Atrial Fibrillation Detection Performance Using Decision Trees, SVM and Artificial Neural Network

Sieciński, Szymon; Kostka, P.; Tkacz, Ewaryst

doi:10.1007/978-3-030-11890-7_65

Cited by 2 publications

(1 citation statement)

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“…Three different models are using artificial neural network (ANN), binary decision tree, and SVM on 10 ECG signals are used for comparison. The best classifier for Atrial Fibrillation was a binary decision tree, which split signal equal to 100 and the worst case is SVM while using one feature [29].…”

Section: Methodsmentioning

confidence: 99%

Detection of Atrial Fibrillation Using a Machine Learning Approach

et al. 2020

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The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

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Section: Methodsmentioning

confidence: 99%