Data compression of the ECG using neural network for digital Holter monitor

Author

Iwata, A. ; Nagasaka, Y. ; Suzumura, N.

Author_Institution

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

Volume

9

Issue

3

fYear

1990

Firstpage

53

Lastpage

57

Abstract

A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.<>

Keywords

data compression; electrocardiography; medical diagnostic computing; neural nets; patient monitoring; activation levels; artificial neural networks; backpropagation algorithm; common waveform features; consecutive heartbeat; current signals; data-compression algorithm; digital Holter recording; electrocardiogram; hidden layer; input signals; input units; interconnecting weights; learning; supervised signals; three-layer ANN; Artificial neural networks; Backpropagation algorithms; Data compression; Digital signal processors; Electrocardiography; Electrodes; Heart beat; Monitoring; Neural networks; Thorax;

fLanguage

English

Journal_Title

Engineering in Medicine and Biology Magazine, IEEE

Publisher

ieee

ISSN

0739-5175

Type

jour

DOI

10.1109/51.59214

Filename

59214