Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

Ambulatory pH monitoring of pathological reflux is an opportunity to observe the relationship between symptoms and exposure of the esophagus to acidic or non-acidic refluxate. This paper describes a method for the development, manufacturing, and implantation of a miniature wireless-enabled pH sensor. The sensor is designed to be implanted endoscopically with a single hemostatic clip. A fully passive rectenna-based receiver based on a zero-bias Schottky diode is also constructed and tested. To construct the device, a two-layer printed circuit board and off-the-shelf components were used. A miniature microcontroller with integrated analog peripherals is used as an analog front end for the ion-sensitive field-effect transistor (ISFET) sensor and to generate a digital signal which is transmitted with an amplitude shift keying transmitter chip. The device is powered by two primary alkaline cells. The implantable device has a total volume of 0.6 cm³ and a weight of 1.2 grams, and its performance was verified in an ex vivo model (porcine esophagus and stomach). Next, a small footprint passive rectenna-based receiver which can be easily integrated either into an external receiver or the implantable neurostimulator, was constructed and proven to receive the RF signal from the implant when in proximity (20 cm) to it. The small size of the sensor provides continuous pH monitoring with minimal obstruction of the esophagus. The sensor could be used in routine clinical practice for 24/96 h esophageal pH monitoring without the need to insert a nasal catheter. The "zero-power" nature of the receiver also enables the use of the sensor for automatic in-vivo calibration of miniature lower esophageal sphincter neurostimulation devices. An active sensor-based control enables the development of advanced algorithms to minimize the used energy to achieve a desirable clinical outcome. One of the examples of such an algorithm would be a closed-loop system for on-demand neurostimulation therapy of gastroesophageal reflux disease (GERD).