Neural network based feature extraction scheme for heart rate variability

Ben Raymond; Doraisamy Nandagopal; Jagan Mazumdar; D. Taverner

doi:10.1117/12.205201

6 April 1995 Neural network based feature extraction scheme for heart rate variability

Ben Raymond, Doraisamy Nandagopal, Jagan Mazumdar, D. Taverner

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205201
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Ben Raymond, Doraisamy Nandagopal, Jagan Mazumdar, and D. Taverner "Neural network based feature extraction scheme for heart rate variability", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205201

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