The purpose of this paper is to develop and validate a miniature system that can wirelessly acquire gastric electrical activity called slow waves, and deliver high energy electrical pulses to modulate its activity. The system is composed of a front-end unit, and an external stationary back-end unit that is connected to a computer. The front-end unit contains a recording module with three channels, and a single-channel stimulation module. Commercial off-the-shelf components were used to develop front- and back-end units. A graphical user interface was designed in LabVIEW to process and display the recorded data in real-time, and store the data for off-line analysis. The system was successfully validated on bench top and in porcine models. The bench-top studies showed an appropriate frequency response for analog conditioning and digitization resolution to acquire gastric slow waves. The system was able to deliver electrical pulses at amplitudes up to 10 mA to a load smaller than 880 Ω. Simultaneous acquisition of the slow waves from all three channels was demonstrated . The system was able to modulate -by either suppressing or entraining- the slow wave activity. This study reports the first high-energy stimulator that can be controlled wirelessly and integrated into a gastric bioelectrical activity monitoring system. The system can be used for treating functional gastrointestinal disorders.

We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects' fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals (PPIs) are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly-varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-minute pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.97, 0.98, 0.97, respectively, which are higher than those of the previous AF algorithm.