Key result
A hybrid technique using adaptive learning and feature selection outperformed conventional approaches for classifying cardiac arrhythmias.
An adaptive learning and feature selection algorithm improves ECG arrhythmia classification compared to conventional methods, offering a promising tool for remote cardiac monitoring.
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May aid remote arrhythmia monitoring; hypothesis-generating pending prospective clinical validation.
Coronary heart disease is being identified as the largest single cause of death along the world. The aim of a cardiac clinical information system is to achieve the best possible diagnosis of cardiac arrhythmias by electronic data processing. Cardiac information system that is designed to offer remote monitoring of patient who needed continues follow up is demanding. However, intra- and interpatient electrocardiogram (ECG) morphological descriptors are varying through the time as well as the computational limits pose significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is, therefore, a promising new intelligent diagnostic tool.
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Bashir et al. (2012) studied Cardiac arrhythmias. Adaptive learning and adaptive feature selection vs. Conventional approaches was evaluated on Classification of different arrhythmias. A hybrid technique using adaptive learning and feature selection outperformed conventional approaches for classifying cardiac arrhythmias.
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