Electrical and Electronic Engineering - Masters by Research Theses
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Item Low-power real-time seizure monitoring via AI-assisted sonification of neonatal EEG(University College Cork, 2024) Nguyen, Tien Van; Popovici, Emanuel; Temko, Andriy; QualcommSeizures in newborns are most frequently provoked by an acute brain injury or systemic insult, making them predominantly acute symptomatic in nature. Early identification and intervention are essential to reducing the risks of mortality and long-term disabilities. Continuous electroencephalogram (EEG) monitoring remains the gold standard for accurate neonatal seizure detection. This procedure is expensive, time-consuming, and requires a high level of expertise. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown substantial promise in expediting seizure diagnosis. These algorithms can provide decision assistance with expert-level accuracy and thus are increasingly used for EEG analysis. However, the lack of transparency and explainability in the decision-making process of ML algorithms hinders their widespread adoption in medical settings. Researchers at the Embedded.Systems@UCC Group proposed a novel method that integrates machine learning with sonification, an alternative EEG interpretation approach by converting the signals into sound, to address the black-box problem while simultaneously improving seizure detection performance. The study also found that human listeners achieved better performance using AI-assisted audio analysis compared to using the ML seizure detection algorithm alone. This study presents a real-time processing design that allows the AI-integrated sonification method to operate in parallel with EEG acquisition. The new design eliminates the operational time delay associated with offline processing in the original study. This is particularly beneficial when the diagnosis is time-critical or in a busy environment where medical professionals have a tight schedule. A low-power implementation of the proposed real-time system is also presented. This implementation, featuring a system-on-chip with a deep neural networks accelerator, has an average power consumption of 13mW. It can be powered using a battery or a general USB port, allowing for a compact design, suitable for deployment in space-constrained environments like the neonatal intensive care units (NICU).Item Analog mixed signal IC design for magnetic tracking in surgery: low area clocking for CT∆ΣM ADCs for in-vivo sensing(University College Cork, 2024) Ferro, Alessandro; Cantillon-Murphy, Padraig; O'Hare, DanielThe application for this work is an in-vivo sensor system for capturing magnetic signals used for tracking surgical instruments such as catheters in the body during minimally invasive surgical procedures. With the ascent of image-guided interventions, the precision in determining instrument pose has become paramount. The proposed system leverages low-frequency electromagnetic fields for magnetic tracking, ensuring non-ionising, line-of-sight-free measurements with millimetre-scale accuracy. The miniaturisation demands of electromagnetic sensors, crucial to this endeavour, necessitate dimensions less than 0.5 mm in diameter, with the entire system fitting within an area of 1 mm x 0.5 mm. Continuous Time Delta Sigma (CT∆ΣM) ADCs emerge as a promising solution given their power efficiency and reduced requirements on peripheral circuits. These ADCs boast advantages like built-in anti-alias filtering and simplified input buffer requirements. However, their susceptibility to clock jitter, especially in single-bit versions, presents challenges. This research introduces an original MATLAB and Simulink model for the CT∆ΣM. The model initially accounted for clock jitter employing a white noise spectrum. However, subsequent analysis unearthed a profound limitation: while FIR filters within the CT∆ΣM reduced periodic jitter’s impact, they were vulnerable to non-white noise spectrums. Although present in CT∆ΣM literature, this significant observation was previously uncharted for the signal bandwidth applications akin to this work. To address this, the research delves into creating a comprehensive Phase noise spectrum model using MATLAB. This model elucidates the profound limitations imposed on the ADC’s in-band noise by the clock source’s phase noise spectrum, a revelation that reshaped our understanding of the CT∆ΣM’s constraints. Originally, a detailed schematic design for the clock source was undertaken with the primary objective of optimising area and power consumption, relying on a Resistor Capacitor based clock source for the Frequency-Locked Loop (FLL). The prevalent belief was that the typical phase noise spectrum of clock sources would have a negligible influence on the CT∆ΣM ADC. However, post-chip measurement analysis dispelled this notion. This research discovered that the clock source’s phase noise spectrum profoundly affected the CT∆ΣM ADC’s performance, highlighting an overlooked yet critical interplay.Item Development of a wearable system for monitoring people with Parkinson’s at home(University College Cork, 2023) Sica, Marco; O'Flynn, Brendan; Tedesco, Salvatore; AbbVie; Enterprise IrelandParkinson's disease (PD) is a neurodegenerative disorder affecting the central nervous system. Besides impairing motor functions, PD is also characterized by a broad variety of non-motor symptoms, such as mood and cognitive disorders, hallucinations, and sleep disturbances. People with Parkinson’s (PwP) are evaluated using clinical assessments and self-administered diaries and, as a consequence, they receive the necessary pharmacological therapy to alleviate symptoms and enhance sleep quality. Tri-axial accelerometers and gyroscopes might be employed to objectively evaluate Parkinsonians’ condition and help clinicians in making decisions. PwP often have significant abnormalities in blood pressure (BP) due to comorbid age-related cardiovascular disease and orthostatic hypotension, which result in blurred vision, dizziness, syncope, and falls. Frequent BP monitoring may aid in the evaluation of such events and differentiate PD symptoms from those originated by hypotension. A number of commercially available devices designed specifically for PwP include accelerometers and gyroscopes for the estimation of main motor symptoms, gait parameters, and sleep quality; nevertheless, according to the authors' knowledge, neither commercially available systems nor published works include also photoplethysmograph (PPG) and electrocardiogram (ECG) sensors that can be used for Parkinsonians’ cardiovascular monitoring. In this work, the WESAA (Wearable Enabled Symptom Assessment Algorithm) system is introduced as a revolutionary tool for the remote monitoring of PD patients. It is comprised of two devices worn on the wrist and ankle, and its key purpose is to capture accelerations and angular velocities from these body parts, as well as PPG and ECG data. This information may be used off-line to predict common PD motor symptoms (such as tremor, bradykinesia, and dyskinesia), walking speed, sleep-wake cycles, and cuff-less BP measures. The system requirements, market overview, industrial design, hardware and firmware development, user experience, early results of the gathered inertial raw data, and validation of the PPG and ECG signals were looked at in detail in the present work. The created system fulfils all the defined user requirements, and the sensors used yielded results equivalent to gold standard technologies. This thesis also studied the PwP's viewpoints on the WESAA system which is crucial for usability and adherence, examining practical concerns such as size, design, and comfort, as well as emotional consequences, societal impact, and the significance of discretion. In addition, users discussed their data-sharing preferences and how wearable technology could enhance their lives (i.e., the necessity to give feedback, particularly on motor symptoms). The WESAA system thus presents a promising alternative for remote monitoring of PwP since it has the ability to assist physicians in decision-making in terms of medication and treatment, giving them potentially useful information about the motor symptoms and the overall health status of their patients. Future work involves the implementation of off-line solutions for the detection of PD motor symptoms, walking speed, sleep-wake cycles, and cuff-less BP; machine learning algorithms should be adopted and a broader data collection carried out.Item Low-cost integration technologies for next generation wearable devices(University College Cork, 2023) Federico, Andrea; O'Flynn, Brendan; Belcastro, Marco; Enterprise Ireland; DeRoyal Industries; Science Foundation IrelandIn recent years, the commercial and research interest in the development of textile based products incorporating embedded micro-electronic elements, such as sensors, microcontrollers batteries etc., also known as e-textiles, has grown considerably. This has resulted in significant progress in researching new methods of electronics integration and the utilisation of wearable systems in a variety of applications in different fields. However, despite the results obtained to date, the requirements associated with e-textile applications, such as being lightweight, small in size, and stretchable still present a significant engineering challenge and topic of interest to the research community. Moreover, due to their close contact with the human body, the design of e-textile devices must consider several aspects related to the user experience associated with such wearable systems. As a consequence, freedom of movement, non-intrusiveness and overall comfortability must be guaranteed to ensure uptake of the technology in a consumer product setting. Over the past decade, a variety of strategies for textile electronics integration have been investigated and developed by the research community. New strategies for the use of conductive materials have been developed to replace the use of traditional conductive wires or cables and thus improve system wearability and durability, since the presence of cables can restrict body movements and prevent the execution of specific physical tasks. Such novel materials are increasingly accessible to system designers in the form of conductive threads, fabrics and inks which open up new opportunities for wearable electronic systems. There are different methods of integrating rigid and flexible electronic components into/onto textiles for the development of wearable e-textile systems, including chemical, physical and mechanical strategies. In this work, we present a review of the state of the art in the research literature as well as in commercially available products regarding the main integration strategies available to system designers. Moreover, an evaluation of and performance’s analysis of novel conductive materials is presented, as well as an implementation of a low-cost integration technique for e-textiles and wearable sensing systems. As a proof of concept and validation activity, two system demonstrators are presented: a full-body suit which is able to capture the EMG signals coming from the lower body through conductive traces; and an inflatable cuff embedding standard electronic modules with integrated sensing actuation and communication developed as a medical device. Both the demonstrators integrate flexible substrates electronic components, sensors and power supplies. Future work will involve further testing on the reliability of the low-cost integration technique presented in Chapter 3 and, furthermore, the integration of flexible electronics and sensors onto the demonstrator smart compression system medical device, allowing for better monitoring of the wounds status and, therefore, better evaluation of the healing process associated with the use of compression bandages in a clinical setting.Item Implementation of an AI-assisted sonification algorithm on an edge device(University College Cork, 2023) O'Sullivan, Feargal; Popovici, Emanuel; Temko, Andriy; QualcommOxygen deprivation at birth leads to brain injury, which can have serious consequences. It is the dominant cause of seizures. Quickly and accurately detecting seizures is a challenging problem for neonates. A severe shortage of medical professionals with the necessary expertise for Electroencephalogram (EEG) analysis leads to significant delays in decision-making and hence treatment. These problems are made worse in disadvantaged communities. Artificial intelligence (AI) techniques have been proposed to automate the process and compensate for the lack of available expertise. However, these models are ’black boxes', and their lack of explainability dampens the wide adoption by medical professionals. AI-assisted sonification adds explainability to any such automated methodology, empowering medical professionals to make accurate decisions regardless of their level of expertise in EEG analysis. The feasibility of an implementation of an AI-assisted sonification algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations. Furthermore, a neural network is analysed for the potential of low-precision implementation, enabling inference on specialised hardware.