Wireless body area networks promise to revolutionize health monitoring by allowing the transition from centralized health care services to ubiquitous and pervasive health monitoring in every-day life. One of the major challenges in the design of such systems is the energy consumption as wireless body area networks are battery-powered. Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls behind the performance attained by state-of-the-art wavelet-based algorithms. This is mainly because current CS-based algorithms exploit only the sparsity of the signal, ignoring important signal structure information that can be known a priori and lead to enhanced reconstruction results.;This dissertation presents methods to exploit prior knowledge of the ECG in order to improve the reconstruction quality and to increase the compression rates offered by current CS-based algorithms. First, we describe an algorithm that exploits prior information about the wavelet dependencies across scales and the high fraction of common support of the wavelet coefficients of consecutive ECG segments.;One of the main challenges in the reconstruction of ECG signals via CS-based algorithms is the recovery of the small-magnitude wavelet coefficients. This dissertation also presents a weighted ℓ1 minimization algorithm, based on a maximum a posteriori (MAP) approach, that exploits the exponentially decaying magnitude of the detail coefficients across scales and the accumulation of signal energy in the approximation subband.;In real scenarios, ECG recordings are often corrupted by artifacts. This dissertation also presents a robust reconstruction method for ECG signals in the presence of electromyographic noise. To achieve this objective, robust statistics are used to develop appropriate methods addressing the problem of electromyographic noise, which can be modeled as impulsive noise.;Most prior work in CS ECG has employed analytical sparsifying transforms such as wavelets. Another contribution of this dissertation is to adaptively learn a sparsifying transform (overcomplete dictionary) that exploits the multi-scale sparse representation of ECG signals. By calculating subdictionaries at different data scales, we are able to exploit the correlation within each wavelet subband and, subsequently, represent the data in a more efficient manner.;Generic sparsity models that are not tied to a specified structure are also explored in this dissertation. More precisely, restricted Boltzmann machines and deep belief networks are employed to model the sparsity pattern of ECG signals with the goal of exploiting higher-order statistical dependencies between sparse coefficients.;The effectiveness of the proposed algorithms is demonstrated on real ECG signals from the MIT-BIH Arrhythmia Database. Results show that the proposed algorithms require fewer measurements and offer superior reconstruction accuracy than existing CS-based methods for ECG compression.
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