This paper presents an ECG compression algorithm based on the optimal selection of wavelet filters and
threshold levels in different subbands that achieve maximum data volume reduction while guaranteeing
reconstruction quality. The proposed algorithm starts by segmenting the ECG signal into frames; where
each frame is decomposed into m subbands through optimized wavelet filters. The resulting wavelet
coefficients are thresholded and those having absolute values below specified threshold levels in all
subbands are deleted and the remaining coefficients are appropriately encoded with a modified version of
the run-length coding scheme. The threshold levels to use, before encoding, are adjusted in an optimum
manner, until predefined compression ratio and signal quality are achieved. Extensive experimental
tests were made by applying the algorithm to ECG records from the MIT-BIH Arrhythmia Database.
The compression ratio (CR), the root-mean-square difference (PRD) and the zero-mean percent rootmean-
square difference (PRD1) measures are used for measuring the algorithm performance (high CR
with excellent reconstruction quality). From the obtained results, it can be deduced that the performance
of the optimized signal dependent wavelet outperforms that of Daubechies and Coiflet standard wavelets.
However, the computational complexity of the proposed technique is the price paid for the improvement
in the compression performance measures.