The recording of biosignals enables physicians to correctly diagnose diseases and prescribe treatment. Existing wireless systems failed to effectively replace the conventional wired methods due to their large sizes, high power consumption, and the need to replace batteries. This thesis aims to alleviate these issues by presenting a series of wireless fully-passive sensors for the acquisition of biosignals: including neuropotential, biopotential, intracranial pressure (ICP), in addition to a stimulator for the pacing of engineered cardiac cells. In contrast to existing wireless biosignal recording systems, the proposed wireless sensors do not contain batteries or high-power electronics such as amplifiers or digital circuitries. Instead, the RFID tag-like sensors utilize a unique radiofrequency (RF) backscattering mechanism to enable wireless and battery-free telemetry of biosignals with extremely low power consumption. This characteristic minimizes the risk of heat-induced tissue damage and avoids the need to use any transcranial/transcutaneous wires, and thus significantly enhances long-term safety and reliability. For neuropotential recording, a small (9mm x 8mm), biocompatible, and flexible wireless recorder is developed and verified by in vivo acquisition of two types of neural signals, the somatosensory evoked potential (SSEP) and interictal epileptic discharges (IEDs). For wireless multichannel neural recording, a novel time-multiplexed multichannel recording method based on an inductor-capacitor delay circuit is presented and tested, realizing simultaneous wireless recording from 11 channels in a completely passive manner. For biopotential recording, a wearable and flexible wireless sensor is developed, achieving real-time wireless acquisition of ECG, EMG, and EOG signals. For ICP monitoring, a very small (5mm x 4mm) wireless ICP sensor is designed and verified both in vitro through a benchtop setup and in vivo through real-time ICP recording in rats. Finally, for cardiac cell stimulation, a flexible wireless passive stimulator, capable of delivering stimulation current as high as 60 mA, is developed, demonstrating successful control over the contraction of engineered cardiac cells. The studies conducted in this thesis provide information and guidance for future translation of wireless fully-passive telemetry methods into actual clinical application, especially in the field of implantable and wearable electronics.

Microwave tomography (MWT) differs from the current forms of biomedical imaging modalities by measuring the dielectric properties in different tissues in order to create an image of the object under evaluation. This technology could be harnessed for the evaluation of a stroke because the areas of the brain that are not being provided oxygen will have a reduced concentration of blood, leading to a reduced relative permittivity (also referred to as dielectric constant). Strokes themselves require accurate diagnosis for proper treatment to be administered. Microwave tomography offers advantages of stroke diagnosis over imaging methods such as magnetic resonance imaging (MRI) and computerized tomography (CT). Like MRIs, microwave tomography passes only non-ionizing radiation through the patient, allowing for multiple safe scans. Because MWT requires only an array of antennas sending a non-ionizing electromagnetic field, which is on the level of the fields sent in cell phones, a patient undergoing a stroke could be diagnosed inside an ambulance with multiple MWT scans, greatly reducing the time before treatment. The challenge for this thesis is to correctly solve an ill-posed problem presented in a microwave tomography system and output an image of the object's electrical properties. The system itself is an inverse problem because the object to be imaged and its properties are unknown. Rather, the incident field and resulting scattered field due to interaction with the object of interest are known. To achieve a unique solution for this problem, a software implementation of a common microwave inversion method known as Born's Iterative Method is realized through MATLAB.