DocumentCode :
2723745
Title :
Compression of morphologically similar ECG complexes using neural networks
Author :
Hamilton, D.J. ; Sandham, W.A. ; Thomson, D.C.
Author_Institution :
Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow, UK
fYear :
1995
fDate :
34759
Firstpage :
42644
Lastpage :
42649
Abstract :
The authors outline a compression approach capable of achieving low bit rates while reconstructing the signal with a high fidelity. The approach does not involve any clinical classification or parameterisation. It can be seen from the results presented that the use of this neural network compression system can provide excellent compression performance, especially where low bit rates and low reconstruction errors are required. Current work is focusing on a full implementation of the compression system, including the precompression classification stage. Particular emphasis is now being placed on the compression of low SNR ECG signals such as those found in a realistic ambulatory recording environment
Keywords :
data compression; electrocardiography; medical signal processing; neural nets; clinical classification; compression performance; electrodiagnostics; high fidelity signal reconstruction; low SNR ECG signals; low bit rates; low reconstruction errors; morphologically similar ECG complexes compression; neural network compression system; parameterisation; precompression classification stage; realistic ambulatory recording environment;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Signal Processing in Cardiography, IEE Colloquium on
Conference_Location :
London
Type :
conf
DOI :
10.1049/ic:19950282
Filename :
478260
Link To Document :
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