Key result
DWT reduces maximum reconstruction error by ~53% vs DCT for EMG signal compression.
Why the study?
A comparative study was needed to identify the most efficient transform method for vector quantization-based compression of EMG signals.
For EMG signal compression, combining discrete wavelet transform with vector quantization and SPIHT coding outperforms discrete cosine transform.
DWT-based EMG compression may aid signal fidelity in monitoring; leaves open prospective validation before clinical use.
In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT.
No takes yet. Share an insight, caveat, or question.
Ntsama et al. (2016) studied Healthy (EMG signal collection) (n=4). Discrete Wavelet Transform (DWT) based compression vs. Discrete Cosine Transform (DCT) based compression was evaluated on Percentage Root Mean Square Difference (PRD) and Signal to Noise Ratio (SNR) at various Compression Factors. The compression method based on the Discrete Wavelet Transform (DWT) was more efficient for EMG signals than the Discrete Cosine Transform (DCT), yielding a lower maximum reconstruction error rate (1.60% vs 3.4%).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: