Key result
Weighted-compressive sensing matches discrete wavelet transform ECG reconstruction with substantially lower computational cost.
Why the study?
ECG signals are highly sparse in the frequency domain with slowly changing sparsity, motivating investigation of weighted-compressive sensing for efficient reconstruction.
May aid efficient ECG reconstruction in bandwidth-constrained settings; leaves open prospective clinical validation before routine use.
The potential of the new weighted-compressive sensing approach for efficient reconstruction of electrocardiograph (ECG) signals is investigated. This is motivated by the observation that ECG signals are hugely sparse in the frequency domain and the sparsity changes slowly over time. The underlying idea of this approach is to extract an estimated probability model for the signal of interest, and then use this model to guide the reconstruction process. The authors show that the weighted-compressive sensing approach is able to achieve reconstruction performance comparable with the current state-of-the-art discrete wavelet transform-based method, but with substantially less computational cost to enable it to be considered for use in the next generation of miniaturised wearable ECG monitoring devices.
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Zonoobi et al. (2014) studied ECG signal reconstruction. Weighted-compressive sensing approach vs. Discrete wavelet transform-based method was evaluated on Reconstruction performance and computational cost. The weighted-compressive sensing approach achieved ECG reconstruction performance comparable to the discrete wavelet transform-based method but with substantially less computational cost.
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