Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms

Date

1996-11

Author

Baykal, Nazife
Yalabık, N.
Erkmen, Aydan Müşerref

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Doppler umbilical artery blood flow velocity waveform measurements are used in perinatal surveillance for the evaluation of fetal condition. There is an ongoing debate on the predictive value of Doppler measurements concerning the critical effect of the selection of parameters for the interpretation of Doppler waveforms. In this paper, we describe how neural network methods can be used both to discover relevant classification features and subsequently to; classify Doppler umbilical artery blood flow velocity waveforms. Results obtained from 199 normal and high risk patients' umbilical artery waveforms highlighted a classification concordance varying from 90 to 98% accuracy. Copyright (C) 1996 Elsevier Science Ltd.

Subject Keywords

Doppler Umbilical Artery Blood Flow Velocity Waveforms, Image Processing, Feature Extraction, Pattern Classification

URI

https://hdl.handle.net/11511/52498

Journal

COMPUTERS IN BIOLOGY AND MEDICINE

DOI

https://doi.org/10.1016/S0010-4825(96)00018-2

Collections

Graduate School of Informatics, Article

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Citation Formats

N. Baykal, N. Yalabık, and A. M. Erkmen, “Feature discovery and classification of Doppler umbilical artery blood flow velocity waveforms,” COMPUTERS IN BIOLOGY AND MEDICINE, pp. 451–462, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52498.