Why the study?
Does a short-time multifractal approach with a fuzzy neural network accurately detect and classify arrhythmias from ECG records?
Does a short-time multifractal approach with a fuzzy neural network accurately detect and classify arrhythmias from ECG records?
A novel short-time multifractal approach using a fuzzy Kohonen network can accurately and rapidly detect and classify arrhythmias from ECG records.
Supports fast ECG arrhythmia classifiers; leaves open prospective validation before clinical adoption.
We have proposed the notion of short-time multifractality and used it to develop a novel approach for arrhythmia detection. Cardiac rhythms are characterized by short-time generalized dimensions (STGDs), and different kinds of arrhythmias are discriminated using a neural network. To advance the accuracy of classification, a new fuzzy Kohonen network, which overcomes the shortcomings of the classical algorithm, is presented. In our paper, the potential of our method for clinical uses and real-time detection was examined using 180 electrocardiogram records [60 atrial fibrillation, 60 ventricular fibrillation, and 60 ventricular tachycardia]. The proposed algorithm has achieved high accuracy (more than 97%) and is computationally fast in detection.
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Wang et al. (2001) studied this question.
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