Data compression using neural network for digital Holter monitor

Author

Nagasaka, Yasunori ; Iwata, Akira ; Suzumura, Nobuo

Author_Institution

Dept. of Electr. & Comput. Eng., Nagoya Inst. of Technol., Japan

fYear

1989

fDate

9-12 Nov 1989

Firstpage

2019

Abstract

A data compression algorithm for digital Holter recording using artificial neural networks is proposed. Dual three-layer (one hidden layer) neural networks with only a few units in the hidden layer are used. The networks are tuned with supervised signals that are the same as input signals. Back-propagation is used for the learning process. Network 1 performs data compression, and network 2 is learning with current signals. If the waveform of the electrocardiogram changes, network 2 is copied to network 1. The activation levels of hidden layer units are an encoded representation of the input signal waveforms. The original waveforms can be reproduced from the activation levels using the network between the hidden and output layers

Keywords

computerised monitoring; data compression; electrocardiography; learning systems; medical diagnostic computing; neural nets; activation levels; artificial neural networks; back propagation; data compression algorithm; digital Holter monitor; electrocardiogram; hidden layer units; input signal waveforms; learning process; original waveforms; output layers; supervised signals; Artificial neural networks; Computer networks; Computerized monitoring; Data compression; Detectors; Digital integrated circuits; Digital magnetic recording; Magnetic recording; Neural networks; Thorax;

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.1989.96574

Filename

96574