Key result
A hybrid adaptive algorithm for S-EMG signal compression decreased the per cent residual difference by at least 5% for isometric signals and 3.6% for isotonic signals compared to standard algorithms.
A novel hybrid adaptive algorithm using wavelet transform and neural networks improves the compression of surface EMG signals compared to standard methods.
May optimize EMG data handling; leaves open validation before cardiovascular monitoring use.
This paper presents a hybrid adaptive algorithm for the compression of surface electromyographic (S-EMG) signals recorded during isometric and/or isotonic contractions. This technique is useful for minimizing data storage and transmission requirements for applications where multiple channels with high bandwidth data are digitized, such as telemedicine applications. The compression algorithm proposed in this work uses a discrete wavelet transform for spectral decomposition and an intelligent dynamic bit allocation scheme implemented by an approach using the Kohonen layer, which improves the bit allocation for sections of the S-EMG with different characteristics. Finally, data and overhead information are packed by entropy coding. The results for the compression of isometric EMG signals showed that this algorithm has a better performance than standard wavelet compression algorithms presented in the literature (presenting a decrease of at least 5% in per cent residual difference (PRD) for the same compression ratio), and a performance that is comparable with the performance of algorithms based on an embedded zero-tree wavelet. For isotonic EMG signals, its performance is better than the performance of the algorithms based on embedded zero-tree wavelets (presenting a decrease in PRD of about 3.6% for the same compression ratios, in the useful compression range).
No takes yet. Share an insight, caveat, or question.
Berger et al. (2006) studied this question. Hybrid adaptive algorithm using discrete wavelet transform and Kohonen layer vs. Standard wavelet compression algorithms and embedded zero-tree wavelet algorithms was evaluated on Per cent residual difference (PRD) for the same compression ratio. A hybrid adaptive algorithm for S-EMG signal compression decreased the per cent residual difference by at least 5% for isometric signals and 3.6% for isotonic signals compared to standard algorithms.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: