Information Theory Based Classification Method and its Application In Atrial Fibrillation Detection

Date

2019-12

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Abstract

A novel mutual information-based classification (MIC) method is introduced. MIC selects features that carry the highest mutual information from the set of all available features. In testing, MIC combines the test subject with the training data and searches for the class of the test subject that maximizes the mutual information of the combination of that test subject and the training data. The proposed MIC method is tested in the detection of atrial fibrillation (AF). Features are extracted from a single lead ECG signal using known methods. Numerical results presented show that MIC can perform significantly better than support vector machines (SVM). Accuracies over 90% are reported using MIC with only few features selected according to mutual information.

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Keywords

Classification, Information theory, Machine learning

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©2019 Sebastian Pradeep Fonseka. All Rights Reserved.

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