Key result
A classification system using matched wavelets with neural network and SVM classifiers improved sensitivity and specificity for atrial fibrillation detection compared to standard wavelet functions.
Why the study?
Does a machine learning classifier using matched wavelets analysis improve the detection of atrial fibrillation from ECG signals compared to standard wavelet functions?
Does a machine learning classifier using matched wavelets analysis improve the detection of atrial fibrillation from ECG signals compared to standard wavelet functions?
A machine learning approach using matched wavelets analysis with NN and SVM classifiers improves the sensitivity and specificity of atrial fibrillation detection from ECG signals.
Hypothesis-generating for matched wavelet AF classifiers; leaves open clinical validation in prospective ECG studies.
Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to extract and transform the representation of knowledge gathered in Black Box parameters during classifier learning phase to be better and natural understandable for human user/expert. Proposed system was tested on the set of ECG signals of 20 atrial fibrillation (AF) and 20 control group (CG) patients, divided into learning and verifying subsets, taken from MIT-BiH database. Obtained results showed, that the ability of generalization of created system, expressed as a measure of sensitivity and specificity increased, due to extracting and selectively choosing only the most representative features for analyzed AF detection problem. Classification results achieved by means of constructed matched wavelet, created for given AF detection features were better than indicators obtained for standard wavelet basic functions used in ECG time-frequency decomposition.
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Kostka et al. (2009) studied Atrial fibrillation (n=40). Matched wavelets analysis with Neural Networks (NN) and Support Vector Machine (SVM) classifiers vs. Standard wavelet basic functions was evaluated on Sensitivity and specificity of AF detection. A classification system using matched wavelets with neural network and SVM classifiers improved sensitivity and specificity for atrial fibrillation detection compared to standard wavelet functions.
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