Objective:
To evaluate the performance of five different types of textiles as band electrodes for calf bioimpedance measurements in comparison with conventional spot Ag/AgCl electrodes.
Approach:
Calf bioimpedance measurements were performed in 10 healthy volunteers with five different textile materials cut into bands and Ag/AgCl spot electrodes as a baseline. Collected bioimpedance data were analyzed in terms of precision, fit error and presence of measurement artifacts. Each textile material was also evaluated for participant comfort.
Main Results:
Bioimpedance values for spot electrodes were higher at low frequencies as compared with band electrodes but not at high frequencies. This suggests that spot electrodes have frequency dependent current distributions that adversely impact their use for volume measurements and band electrodes are preferable. The SMP130T-B fabric had the highest precision and the lowest best fit error to the Cole model of the tested textile materials. However, it was the least comfortable textile and most expensive. The Stretch material performed slightly worse than the SMP130T-B fabric, but was half the cost and the most comfortable.
Significance:
These results suggest that there are suitable textile materials for use as dry, band electrodes for calf bioimpedance measurements and that these band electrodes enable greater current uniformity. These textiles could be integrated into a compression sock for remote monitoring of diseases such as Congestive Heart Failure.
A low-power wearable ECG monitoring system has been developed entirely from discrete electronic components and a custom PCB. This device removes all loose wires from the system and minimizes the footprint on the user. The monitor consists of five electrodes, which allow a cardiologist to choose from a variety of possible projections. Clinical tests to compare our wearable monitor with a commercial clinical ECG recorder are conducted on ten healthy adults under different ambulatory conditions, with nine of the datasets used for analysis. Data from both monitors were synchronized and annotated with PhysioNet's waveform viewer WAVE (physionet.org) [1]. All gold standard annotations are compared to the results of the WQRS detection algorithm [2] provided by PhysioNet. QRS sensitivity and QRS positive predictability are extracted from both monitors to validate the wearable monitor.
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