This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

This paper proposes a novel adaptive dictionary (AD) reconstruction scheme to improve the performance of compressed sensing (CS) with electrocardiogram signals (ECG). The method is based on the use of multiple dictionaries, created using dictionary learning (DL) techniques for CS signal reconstruction. The modified reconstruction framework is a two-stage process that leverages information about the signal from an initial signal reconstruction stage. By identifying whether a QRS complex is present and if so, determining a location estimate of the QRS, the most appropriate dictionary is selected and a second stage more refined signal reconstruction can be obtained. The performance of the proposed algorithm is compared with state-of-the-art CS implementations in the literature, as well as the set partitioning in hierarchical trees (SPIHT) wavelet-based lossy compression algorithm. The results indicate that the proposed reconstruction scheme outperforms all existing CS implementations in terms of signal fidelity at each compression ratio tested. The performance of the proposed approach also compares favorably with SPIHT in terms of signal reconstruction quality. Furthermore, an analysis of the overall power consumption of the proposed ECG compression framework as would be used in a body area network (BAN) demonstrates positive results for the proposed CS approach when compared with existing CS techniques and SPIHT.

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Recent research has examined the combination of compressed sensing with over-complete dictionaries for the lossy compression of electrocardiogram (ECG) signals. The application of dictionary learning to automatically create the dictionary is described. A novel analysis of the reconstructed signals using a range of clinical metrics based around QRS feature extraction and heart rate variability is employed. Two methods for dictionary creation are proposed: patient specific and patient agnostic. A detailed comparison of each approach is described. Considering ambulatory ECG monitoring as an application, each methodology is analysed for a wide range of compression ratios.

The purpose of this study is to detect vesicoureteral reflux (VUR) non-invasively using electrical impedance tomography (EIT). VUR is characterized by the backflow of urine from the bladder to the kidneys. Methods: Using porcine models, small quantities of a solution mimicking the electrical properties of urine were infused into each ureter. EIT measurements were taken before, during and after the infusion using electrodes positioned around the abdomen. The collected data from 116 experiments were then processed and time-difference images reconstructed. Objective VUR detection was determined through statistical analysis of the mean change in the voltage signals and EIT image pixel intensities. Results: Unilateral VUR was successfully detected in 94.83% of all mean voltage signals and in over 98.28% of the reconstructed images. The images showed strong visual contrast between the region of interest and the background. Conclusion: In animal models, EIT has the capability to detect reflux in the kidneys with high accuracy. The results show promise for EIT to be used for screening of VUR in children. Significance: VUR is the most common congenital urinary tract abnormality in children. The condition predisposes children to urinary tract infections (UTIs) and kidney damage. The current gold standard diagnostic test, a voiding cystourethrogram (VCUG), is invasive and uses ionizing radiation; therefore, there is a need for new tools for identifying VUR in children. This study presents a non-invasive method to detect VUR in animal models, illustrating the potential for EIT as a screening tool in clinical scenarios.