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%.

In this study, we investigated the relationship between similarity measures and entropy for fuzzy sets. First, we developed fuzzy entropy by using the distance measure for fuzzy sets. We pointed out that the distance between the fuzzy set and the corresponding crisp set equals fuzzy entropy. We also found that the sum of the similarity measure and the entropy between the fuzzy set and the corresponding crisp set constitutes the total information in the fuzzy set. Finally, we derived a similarity measure from entropy and showed by a simple example that the maximum similarity measure can be obtained using a minimum entropy formulation.