access icon openaccess On ECG reconstruction using weighted-compressive sensing

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.

Inspec keywords: electrocardiography; medical signal processing; discrete wavelet transforms; compressed sensing; signal reconstruction; probability

Other keywords: discrete wavelet transform-based method; probability model; miniaturised wearable ECG monitoring devices; weighted-compressive sensing; ECG signals; electrocardiograph signal reconstruction

Subjects: Other topics in statistics; Bioelectric signals; Biology and medical computing; Probability theory, stochastic processes, and statistics; Integral transforms; Integral transforms; Electrodiagnostics and other electrical measurement techniques; Function theory, analysis; Digital signal processing; Signal processing and detection; Electrical activity in neurophysiological processes; Other topics in statistics

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