A Multi-Stage Neural Network Classifier for ECG Events

Abstract

In this paper, a multi-stage network including two multilayer perceptron (MLP) and one self organizing map (SOM) networks is presented. The input of the network is a combination of independent features and the compressed ElectroCardioGram (ECG) data. The proposed network as a form of data fusion, performs better than using the raw data or individual features. We classified six common ECG waveforms using ten ECG records of the MIT/BIH arrhythmia database. An average recognition rate of 0.883 was achieved within a short training and testing time.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410480

Entities

People

  • D. Powers
  • H. G. Hosseini
  • K. J. Reynolds

Organizations

  • Auckland University of Technology

Tags

DTIC Thesaurus Topics

  • Abnormalities
  • Algorithms
  • Classification
  • Clustering
  • Computer-Aided Diagnosis
  • Computing System Architectures
  • Data Fusion
  • Databases
  • Engineering
  • Errors
  • Information Science
  • Machine Learning
  • Neural Networks
  • Recognition
  • Self Organizing Systems
  • Statistical Analysis
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Cardiovascular Physiology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks