title: A short-time multifractal approach for arrhythmia detection based on fuzzy neural network.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Shanghai Jiao Tong University, China. ywang76@sh163c.sta.net.cn
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: Y S
authors.initials: YS
authors.lastname: Zhu
authors.forename: N V
authors.initials: NV
authors.lastname: Thakor
authors.forename: Y H
authors.initials: YH
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification / Q000175:diagnosis*; D004562:Electrocardiography; D017709:Fractals; D017143:Fuzzy Logic; D006801:Humans; D008433:Mathematics; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity