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This paper describes the design, development, and testing of flexible hybrid silicone-textile sensors and a flexible switching circuit that were integrated into a wearable system for monitoring neurodegenerative diseases. A total of 6 planar monopole antenna sensors were fabricated that propagates at two separate resonant frequencies: 800 MHz and 2.1 GHz respectively. In addition, 2 switching circuits, each having 3 switches and 4 SMA breakout boards, were assembled and placed on the wearable neurodegeneration monitoring system. Each switching circuit connects 3 sensors to a single port on a vector network analyser (VNA) that is used to generate and receive microwave signals. Experiments were performed using the wearable device with the developed sensors and switching circuit on phantoms mimicking two common physiological changes in the brain caused by neurodegenerative diseases: 1) brain atrophy and 2) lateral ventricle enlargement. The dual nature of the sensors' resonance allows it to detect both brain atrophy and lateral ventricle enlargement separately at different operating frequency. This provides the advantage of minimising the number of sensor elements needed to monitor neurodegenerative disease. The use of a switching circuit also allows for quick and convenient measurements by choosing which sensors are active for ports 1 and 2 on the VNA respectively. In addition to being low-cost, the flexibility of the materials used in fabrication allows the sensors and switching circuit to be conformal to the patient's head. Results from the experiments indicates that the sensors and switching circuit were working successfully when integrated into the wearable device.

Alzheimer's disease is the most common form of neurodegenerative disease and a leading cause of dementia today. A pathological effect of Alzheimer's disease is brain atrophy, which is the progressive shrinkage of the brain volume and weight. Another effect of Alzheimer's disease is the enlargement of lateral ventricles in the brain. Currently, MRI and CT scanners can detect and show images of the brain during different stages of Alzheimer's disease. However, its limited accessibility, high costs, and static structure make it inconvenient for some to use. This paper presents the design and novel application of a wearable device comprising of flexible microwave antennas, with an operating frequency range of 800 MHz to 2.5 GHz, that detects the progression of brain atrophy and lateral ventricle enlargement in patients with Alzheimer's at the earliest stage possible. The operating principle of the antennas are simulated in near field using CST and the device is experimentally validated using lamb brain samples and samples representing cerebral spinal fluid (CSF). The measured reflection coefficients (S11) and transmission coefficients (S21) were found to correlate with changes in brain volume and changes in CSF volume successfully, thus giving an indication of the progression of Alzheimer's disease in a patient.

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This paper describes a novel approach of detecting different stages of Alzheimer's disease (AD) and imaging betaamyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.