Pro-Detection of Atrial Fibrillation Using Mixture of Experts

Mohamed Ezzeldin A. BASHIR  Kwang Sun RYU  Unil YUN  Keun Ho RYU  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.12   pp.2982-2990
Publication Date: 2012/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.2982
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
atrial fibrillation,  cardiac monitoring,  electrocardiogram (ECG),  training Datase,  

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A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.