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Proceedings Paper

Compression of the electrocardiogram (ECG) using an adaptive orthonomal wavelet basis architecture
Author(s): Janavikulam Anandkumar; Harold H. Szu
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Paper Abstract

This paper deals with the compression of electrocardiogram (ECG) signals using a large library of orthonormal bases functions that are translated and dilated versions of Daubechies wavelets. The wavelet transform has been implemented using quadrature mirror filters (QMF) employed in a sub-band coding scheme. Interesting transients and notable frequencies of the ECG are captured by appropriately scaled waveforms chosen in a parallel fashion from this collection of wavelets. Since there is a choice of orthonormal bases functions for the efficient transcription of the ECG, it is then possible to choose the best one by various criterion. We have imposed very stringent threshold conditions on the wavelet expansion coefficients, such as in maintaining a very large percentage of the energy of the current signal segment, and this has resulted in reconstructed waveforms with negligible distortion relative to the source signal. Even without the use of any specialized quantizers and encoders, the compression ratio numbers look encouraging, with preliminary results indicating compression ratios ranging from 40:1 to 15:1 at percentage rms distortions ranging from about 22% to 2.3%, respectively. Irrespective of the ECG lead chosen, or the signal deviations that may occur due to either noise or arrhythmias, only one wavelet family that correlates best with that particular portion of the signal, is chosen. The main reason for the compression is because the chosen mother wavelet and its variations match the shape of the ECG and are able to efficiently transcribe the source with few wavelet coefficients. The adaptive template matching architecture that carries out a parallel search of the transform domain is described, and preliminary simulation results are discussed. The adaptivity of the architecture comes from the fine tuning of the wavelet selection process that is based on localized constraints, such as shape of the signal and its energy.

Paper Details

Date Published: 6 April 1995
PDF: 15 pages
Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); doi: 10.1117/12.205378
Show Author Affiliations
Janavikulam Anandkumar, George Washington Univ. (United States)
Harold H. Szu, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 2491:
Wavelet Applications II
Harold H. Szu, Editor(s)

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