A comparative study of advanced signal processing techniques for detection of coronary artery disease

Yasemin M. Akay, Metin Akay, Walter Welkowitz, John L. Semmlow, John B. Kostis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Recordings of diastolic heart sound segments were analyzed by using three advanced signal processing techniques: the autoregressive (AR), the autoregressive moving average (ARMA), and the minimum-norm (eigenvector) methods. In order to enhance the diastolic heart sounds and eliminate background noise, the adaptive line enhance (ALE) method was used as a preprocessor. These advanced signal processing techniques were used to estimate the model parameters. The poles of the AR, ARMA and the minimum-norm methods were used to diagnose patients as diseased or normal. Results showed that normal and abnormal records were correctly identified in 32 of 38 (36 patient) cases.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference on Engineering in Medicine and Biology
PublisherPubl by IEEE
Pages2139-2140
Number of pages2
Editionpt 5
ISBN (Print)0780302168
StatePublished - 1991
EventProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, FL, USA
Duration: Oct 31 1991Nov 3 1991

Publication series

NameProceedings of the Annual Conference on Engineering in Medicine and Biology
Numberpt 5
Volume13
ISSN (Print)0589-1019

Other

OtherProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CityOrlando, FL, USA
Period10/31/9111/3/91

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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