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

Yang Wang, Yi Sheng Zhu, Nitish V. Thakor, Yu Hong Xu

Research output: Contribution to journalArticle

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 tachycardial. The proposed algorithm has achieved high accuracy (more than 97%) and is computationally fast in detection.

Original languageEnglish (US)
Pages (from-to)989-995
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume48
Issue number9
DOIs
StatePublished - Sep 3 2001

Keywords

  • Arrhythmia detection
  • Fuzzy neural network
  • Generalized dimension
  • Multifractal

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'A short-time multifractal approach for arrhythmia detection based on fuzzy neural network'. Together they form a unique fingerprint.

  • Cite this