Piezoelectric energy harvesting using blood-flow-like excitation for implantable devices

The goal of this research is to produce a system for powering medical implants to increase the lifetime of the implanted devices and reduce the battery size. The system consists of a number of elements – the piezoelectric material for generating power, the device design, the circuit for rectification and energy storage. The piezoelectric material is analysed and a process for producing a repeatable high quality piezoelectric material is described. A full width half maximum (FWHM) of the rocking curve X-Ray diffraction (XRD) scan of between ~1.5° to ~1.7° for test wafers was achieved. This is state of the art for AlN on silicon and means devices with good piezoelectric constants can be fabricated. Finite element modelling FEM) was used to design the structures for energy harvesting. The models developed in this work were established to have an accuracy better than 5% in terms of the difference between measured and modelled results. Devices made from this material were analysed for power harvesting ability as well as the effect that they have on the flow of liquid which is an important consideration for implantable devices. The FEM results are compared to experimental results from laser Doppler vibrometry (LDV), magnetic shaker and perfusion machine tests. The rectifying circuitry for the energy harvester was also investigated. The final solution uses multiple devices to provide the power to augment the battery and so this was a key feature to be considered. Many circuits were examined and a solution based on a fully autonomous circuit was advanced. This circuit was analysed for use with multiple low power inputs similar to the results from previous investigations into the energy harvesting devices. Polymer materials were also studied for use as a substitute for the piezoelectric material as well as the substrate because silicon is more brittle.

Dynamic classifiers for neonatal brain monitoring

Brain injury due to lack of oxygen or impaired blood flow around the time of birth, may cause long term neurological dysfunction or death in severe cases. The treatments need to be initiated as soon as possible and tailored according to the nature of the injury to achieve best outcomes. The Electroencephalogram (EEG) currently provides the best insight into neurological activities. However, its interpretation presents formidable challenge for the neurophsiologists. Moreover, such expertise is not widely available particularly around the clock in a typical busy Neonatal Intensive Care Unit (NICU). Therefore, an automated computerized system for detecting and grading the severity of brain injuries could be of great help for medical staff to diagnose and then initiate on-time treatments. In this study, automated systems for detection of neonatal seizures and grading the severity of Hypoxic-Ischemic Encephalopathy (HIE) using EEG and Heart Rate (HR) signals are presented. It is well known that there is a lot of contextual and temporal information present in the EEG and HR signals if examined at longer time scale. The systems developed in the past, exploited this information either at very early stage of the system without any intelligent block or at very later stage where presence of such information is much reduced. This work has particularly focused on the development of a system that can incorporate the contextual information at the middle (classifier) level. This is achieved by using dynamic classifiers that are able to process the sequences of feature vectors rather than only one feature vector at a time.