Low-power strategies for signal compression in ambulatory healthcare
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Advances in signal processing are enabling promising solutions for ambulatory monitoring in healthcare applications. A critical objective of such systems is low-power operation to enable long-term battery life in wearable/mobile devices. This project addresses the low-power objective through the use of lossy compression techniques, particularly focusing on Compressed Sensing (CS). CS is a recently-introduced paradigm for compression that has significant potential for deployment in Body Area Networks (BAN) compared to existing compression methods. A particular advantage of CS in this context is the possibility of very simple encoding implementations in the wearable sensors, with greater computational complexity in signal reconstruction. However, the performance of CS remains limited in terms of signal reconstruction quality when compared to existing approaches based on Nyquist sampling. In this thesis, novel encoding strategies for CS are developed and novel Dictionary Learning (DL) techniques are utilised for reconstruction to challenge the current state-of-the-art in this area. The proposed algorithms are analysed in terms of signal fidelity for a given Compression Ratio (CR) and results demonstrate their ability to outperform existing CS approaches. Furthermore, there is increasing interest in Computer Aided Diagnosis (CAD) systems that tolerate some loss in signal fidelity at higher CRs but where diagnostic integrity of the signal is maintained. The proposed algorithms are also evaluated in terms of their ability to maintain performance in CAD systems at higher CRs. Finally, this thesis analyses the power consumption of CS and compares it with a state-of-the-art lossy compression algorithm in terms of overall system power consumption (including power consumption of signal acquisition, digital processing, and wireless transmission) in a BAN. The results confirm the substantial benefits of employing CS as a low-energy encoding implementation.