Prediction of Atrial Fibrillation AMA-IEEE 2011
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m_oThis document presents a study that uses neuro-fuzzy modeling to predict the onset of atrial fibrillation (AF) in postoperative patients using electrocardiogram (ECG) parameters. The researchers developed a neuro-fuzzy network using 15 ECG parameters from 45 subjects. The network consisted of 7 rules and was able to distinguish AF prone patients from controls with good performance. Testing over the last 9 hours before AF onset showed potential for predicting AF onset by monitoring trends in the developed AF prediction index value. The researchers propose refining the model and conducting further testing and a clinical trial to evaluate prediction of AF onset.
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Classifying electrocardiograph waveforms using trained deep learning neural n...
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40120140504003
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This document presents research on classifying cardiac arrhythmias using frequency domain features extracted from electrocardiogram (ECG) signals. Features are extracted from ECG data using Discrete Cosine Transform to calculate the distance between RR waves. These frequency domain features are then classified using various machine learning algorithms, including Classification and Regression Trees, Radial Basis Function networks, Support Vector Machines, and Multilayer Perceptron Neural Networks. Experiments were conducted on the MIT-BIH arrhythmia database to evaluate the performance of the different classifiers.
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