Wearable sectorial electrical impedance tomography and k-means clustering for measurement of gastric processes

Abstract

A low-power and handy gastric data acquisition (g-DAQ) system has been proposed to identify the gastric processes in the epigastric region with sectorial electrical impedance tomography (s-EIT) and K-means sectorial clustering algorithm. The g-DAQ with a wearable abdominal sensor investigates gastric retention levels in the epigastric region during the emptying process. A C-runtime engine with Secure Shell protocol optimized an ARM microprocessor and field programmable gate array-based system with bidirectional channels to perform real-time data acquisition. The s-EIT algorithm projects the gastric conductivity distribution in the epigastric region into a cross-sectional image. K-means clustering method quantitatively identifies the gastric content images on the epigastric region to monitor the different clustered conductivity . The phantom experiments evaluated the s-EIT using liver-shaped, bone-shaped, and gastric-shaped phantoms in an abdominal-shaped vessel to distinguish gastric phantom conductivity. In human experiments, the proposed method was applied to measure 15 samples of the emptying process to evaluate the retention level of liquid gastric content. As a result, the proposed g-DAQ successfully performed a rapid acquisition at least 50 times faster than the conventional method in terms of the data acquisition rate. The developed g-DAQ with 110 mm × 66 mm × 51 mm of dimensions and 0.16 kg of weight took 0.32 sec to measure 208 points per frame and consumed 3.67 Watt of average power operation. By drinking 500 ml of rehydration water with 0.69 S m−1 of conductivity, In subjects 1–4 and phantoms, the maximum R(t) was identified on t 1 as the gastric volume is fully bloated. During 30 min, the emptying liquid content was well-indicated because the minimum R(t) was identified on t 15 as an empty state. The mean of the measurement results is −0.0387 with the linear equation R(t) = −0.0387t + 0.8105. In conclusion, the body mass index did not significantly affect the trendline.