Data compression of the ECG using neural network for digital Holter monitor
- 1 September 1990
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Engineering in Medicine and Biology Magazine
- Vol. 9 (3) , 53-57
- https://doi.org/10.1109/51.59214
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
This publication has 3 references indexed in Scilit:
- An artificial neural network accelerator using general purpose 24 bit floating point digital signal processorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- A digital Holter monitoring system with dual 3 layers neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Learning representations by back-propagating errorsNature, 1986