A short-time multifractal approach for arrhythmia detection based on fuzzy neural network.

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.