Accession Number:

ADA409679

Title:

Classification of Esophageal Motility Records Using Neural Networks

Descriptive Note:

Conference paper

Corporate Author:

MANSOURA UNIV (EGYPT) DEPT OF ELECTRONICS AND COMMUNICATION ENGINEERING

Report Date:

2001-10-25

Pagination or Media Count:

5.0

Abstract:

This paper suggests an automatic diagnostic system for esophageal motility records using neural networks. Signal processing techniques feature extraction and patten recognition criteria were combined to develop computer programs to be used in identifying, characterizing, and classifying of esophageal motility recordings. The architecture of such an automated system includes four cooperating modules a digital filter to remove the interfered noise separation of peristaltic waveforms from the tubular region of the esophagus feature extraction nodule to detect the main quantitative parameters of each esophageal part and a multi-layer feed-forward neural network trained using the conjugate gradient algorithm was used to classify the peristalsis into different categories. The percentage of correct classification reaches 100.

Subject Categories:

  • Anatomy and Physiology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE