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.
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.
Wang et al. (Mon,) studied this question.