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Proceedings Paper

Neural network based feature extraction scheme for heart rate variability
Author(s): Ben Raymond; Doraisamy Nandagopal; Jagan Mazumdar; D. Taverner
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Paper Abstract

Neural networks are extensively used in solving a wide range of pattern recognition problems in signal processing. The accuracy of pattern recognition depends to a large extent on the quality of the features extracted from the signal. We present a neural network capable of extracting the autoregressive parameters of a cardiac signal known as hear rate variability (HRV). Frequency specific oscillations in the HRV signal represent heart rate regulatory activity and hence cardiovascular function. Continual monitoring and tracking of the HRV data over a period of time will provide valuable diagnostic information. We give an example of the network applied to a short HRV signal and demonstrate the tracking performance of the network with a single sinusoid embedded in white noise.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205201
Show Author Affiliations
Ben Raymond, Univ. of Adelaide and Cooperative Research Ctr. for Sensor, Signal and Info. Processing (Australia)
Doraisamy Nandagopal, Cooperative Research Ctr. for Sensor, Signal and Information Processing (Australia)
Jagan Mazumdar, Univ. of Adelaide (Australia)
D. Taverner, Univ. of Adelaide (Australia)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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