Classification of Respiratory Sounds by Using An Artificial Neural Network

Abstract

In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

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Document Details

Document Type
Technical Report
Publication Date
Oct 28, 2001
Accession Number
ADA410264

Entities

People

  • M. C. Sezgin
  • M. Korurek
  • T. Olmez
  • Z. Dokur

Organizations

  • Istanbul Technical University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Simulations
  • Data Sets
  • Electrical Engineering
  • Electronic Mail
  • Engineering
  • Extraction
  • Feature Extraction
  • Machine Learning
  • Measurement
  • Neural Networks
  • Respiration
  • Respiratory Physiological Phenomena
  • Signal Processing
  • Three Dimensional
  • Training

Readers

  • Cardiovascular Physiology
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks