Detection of abnormal electrocardiograms using a neural network approach

Author

Cheung, John Y. ; Hull, Stephen S., Jr.

Author_Institution

Sch. of Electr. Eng. & Comput. Sci. Oklahoma Univ., Norman, OK, USA

fYear

1989

fDate

9-12 Nov 1989

Firstpage

2015

Abstract

The results of using an artificial neural network system (ANNS) for detection and recognition of abnormal electrocardiograms in heart-rate-variability studies are reported. The ANNS is trained initially by standard abnormal EKG patterns. Once the network has been trained, it detects abnormal EKGs in real time with less than 10% error. In this particular case the bidirectional associative memory model was used for the neural network

Keywords

electrocardiography; medical diagnostic computing; neural nets; abnormal electrocardiograms; artificial neural network system; bidirectional associative memory model; detection; heart-rate-variability studies; recognition; Artificial neural networks; Computer science; Frequency domain analysis; Heart beat; Heart rate detection; Heart rate variability; Microprocessors; Neural networks; Neurons; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in

Conference_Location

Seattle, WA

Type

conf

DOI

10.1109/IEMBS.1989.96572

Filename

96572

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1895947