Engineering Science - Theses, Dissertations, and other Required Graduate Degree Essays

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Brain vital signs: Towards next generation neurotechnologies for rapid brain function assessments at point-of-care

Author: 
Date created: 
2019-11-18
Abstract: 

Vital signs such as heart rate, blood pressure and body temperature have revolutionized medical care by providing rapidly assessed, physiology-based, non-invasive and easy-to-understand standardized metrics of different body functions. However, no such vital sign exists for the brain; instead, assessments of the brain are largely reliant on surrogate measures such as observations of behaviour or questionnaire-based measurements, which have been shown to be subjective and unreliable. This research aims to fill this key scientific, clinical, and technological gap by developing a brainwave-based technology platform to evaluate ‘vital sign’ metrics for the brain. A series of studies were undertaken to create and demonstrate a ‘brain vital signs’ platform that is capable of assessing a broad spectrum of functions ranging from the lower-level functions (i.e. sensation) to the highest-level cognition domains (i.e. contextual orientation). In particular, the first study focused on development and initial demonstration of the methods and apparatus for the brain vital signs technology; the next study focused on characterizing the brain vital sign responses to ensure scientific validity; the third study focused on creating a previously non-existant neurophysiology-based neural marker capable of capturing contextual orientation – which is the highest level cognitive domain known to be crucial to frontline clinical assessments; and finally, the last study focused on developing an advanced data analytic technique for maximizing signal capture under noisy environments typical of point-of-care evaluation settings. This research represents the first time that a ‘vital sign’-like metric has been developed for the brain that embodies the key characteristics of existing vital signs, enabling brain function measures that are rapid (~5 minute testing time), easy to use, portable, non-invasive, and standardized with automated analysis. Crucially, these vital sign metrics directly measure the brain’s electrical activity and do not depend on any responses from the test participant, thus providing much more objective information about brain function. The development of portable and objective ‘vital sign’-like metrics for the brain not only advances the scientific understanding of brain function through novel metrics like orientation, but also creates significant opportunities for enhancing clinical diagnosis through improved brain function assessments at the point-of-care.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Ryan D'Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Blink-related oscillations: Neurotechnology advances that open a new window into brain function

Author: 
Date created: 
2019-11-22
Abstract: 

Although spontaneous blinking has traditionally not been considered to have much importance in cognition, increasing behavioural evidence suggests that blinking is modulated by changes in attentional demand and cognitive load. BROs (BROs) are neurophysiological responses occurring after blinking, and have been postulated to originate in the precuneus region known to be involved in environmental monitoring and awareness. Given the importance of the precuneus in supporting consciousness and awareness, BRO responses represent a potential avenue for evaluating consciousness in brain-injured patients. However, BRO studies to date have been hindered by major limitations that compromised the reliability of the findings, leading to this response having been largely dismissed by the scientific community. The current doctoral research aims to address this by investigating the potential of utilizing BRO-based measures to evaluate brain function. The first two studies in this research investigated the temporal, spectral, and neuroanatomical features of the BRO response in healthy adults using high-temporal- and high-spatial-resolution MEG, in controlled sensory environments and utilizing multiple task conditions including both resting and cognitive loading via mental calculation. The third study developed a novel signal analysis technique for extraction of BRO responses using only few sensors to enable the development of a point-of-care platform for BRO assessment. Results showed that BRO responses strongly activate the bilateral precuneus and other regions including the dorsal and ventral visual processing pathways as well as regions of the ventral attention network. There are also concomitant spectral effects consistent with sensory, episodic memory, and information processing following blinking. Crucially, results show that BROs are cognitively-driven brain responses, and that spontaneous blink instances actually represent innate ‘stimulus events’ that are actively processed by the brain, with the effects being modulated by cognitive loading. Together, these findings suggest that BRO responses engage key neural processes and activate important cortical hubs, and represent a novel and intriguing new window into brain function.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Ryan D'Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Automatic analysis of human embryo's early development

Author: 
Date created: 
2019-08-30
Abstract: 

In Vitro Fertilization (IVF) is the most common fertility treatment. In an IVF treatment, embryologists inspect embryos for subjective quality assessment on a daily basis to select one for implantation. There are several biological studies that confirm the correlations between morphological properties of an embryo’s internal structure and its potential in leading to successful implantation. Automated assessment of embryo’s quality enables a more in-depth understanding of such characteristics and their impact on a positive pregnancy outcome. Moreover, automated quality assessment eliminates subjectivity by selecting embryos with the highest implantation potentials. Automatic monitoring and objective quality assessment of human embryo can potentially improve the outcome of IVF process. This can be achieved through unbiased computer-based approaches that can automatically identify various components of an embryo at different growth stages and quantify their characteristics. This dissertation aims to design and develop tools and methodologies for automatic analysis of temporal and morphological aspects of the human embryo’s in vitro development process. Some of these features and components include the number, centroid locations, boundaries of blastomeres, and segmenting regions corresponding to Zona Pellucida, Trophectoderm and Inner Cell Mass. This dissertation takes a crucial step toward achieving automatic embryo quality assessment. The major contributions of this dissertation include the proposal of a novel cell counting and localization method for blastomeres, the development of the first semantic segmentation for blastocyst components outperforming state-of-the-art methods, and the design of the first system to predict implantation outcome from a single blastocyst image outperforming expert embryologists. Experiments are carried out using various criteria to verify the performance of the proposed methods. Furthermore, the methods developed in this Ph.D. dissertation can be utilized to validate various theoretical assumptions about the relationship between morphological and temporal features of the main components of an embryo and the implantation outcome.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Parvaneh Saeedi
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Brain Vital Signs: Towards Next Generation Neurotechnologies for Rapid Brain Function Assessments at Point-of-Care

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2019-11-18
Abstract: 

Vital signs such as heart rate, blood pressure and body temperature have revolutionized medical care by providing rapidly assessed, physiology-based, non-invasive and easy-to-understand standardized metrics of different body functions. However, no such vital sign exists for the brain; instead, assessments of the brain are largely reliant on surrogate measures such as observations of behaviour or questionnaire-based measurements, which have been shown to be subjective and unreliable. This research aims to fill this key scientific, clinical, and technological gap by developing a brainwave-based technology platform to evaluate ‘vital sign’ metrics for the brain. A series of studies were undertaken to create and demonstrate a ‘brain vital signs’ platform that is capable of assessing a broad spectrum of functions ranging from the lower-level functions (i.e. sensation) to the highest-level cognition domains (i.e. contextual orientation). In particular, the first study focused on development and initial demonstration of the methods and apparatus for the brain vital signs technology; the next study focused on characterizing the brain vital sign responses to ensure scientific validity; the third study focused on creating a previously non-existant neurophysiology-based neural marker capable of capturing contextual orientation – which is the highest level cognitive domain known to be crucial to frontline clinical assessments; and finally, the last study focused on developing an advanced data analytic technique for maximizing signal capture under noisy environments typical of point-of-care evaluation settings. This research represents the first time that a ‘vital sign’-like metric has been developed for the brain that embodies the key characteristics of existing vital signs, enabling brain function measures that are rapid (~5 minute testing time), easy to use, portable, non-invasive, and standardized with automated analysis. Crucially, these vital sign metrics directly measure the brain’s electrical activity and do not depend on any responses from the test participant, thus providing much more objective information about brain function. The development of portable and objective ‘vital sign’-like metrics for the brain not only advances the scientific understanding of brain function through novel metrics like orientation, but also creates significant opportunities for enhancing clinical diagnosis through improved brain function assessments at the point-of-care.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Ryan D’Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Abstracting OpenCL for Multi-Application Workloads on CPU-FPGA Clusters

Author: 
Date created: 
2019-12-03
Abstract: 

Field-programmable gate arrays (FPGAs) continue to see integration in data centres, where customized hardware accelerators provide improved performance for cloud workloads. However, existing programming models for such environments typically require a manual assignment of application tasks between CPUs and FPGA-based accelerators. Furthermore, coordinating the execution of tasks from multiple applications necessitates the use of a higher-level cluster management system. In this thesis, we present an abstraction model named CFUSE (Cluster Front-end USEr framework), which abstracts the execution target within a heterogeneous cluster. CFUSE allows tasks from multiple applications from unknown workloads to be mapped dynamically to the available CPU and FPGA resources and allows accelerator sharing among applications. We demonstrate CFUSE with an OpenCL-based prototype implementation for a small cluster of Xilinx FPGA development boards. Using this cluster, we execute a variety of multi-application workloads to evaluate three scheduling policies and to determine the relevant scheduling factors for the system.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Lesley Shannon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment

Author: 
Date created: 
2019-11-04
Abstract: 

Electroencephalography (EEG) records electrical brain activity typically in a non-invasive manner. Recent literature has shown its potential in stroke rehabilitation, to actively engage stroke survivors in rehabilitation. In Chapter 3 of this thesis, the problems of EEG applications in stroke rehabilitation were firstly identified with a pilot study. Two main challenges were identified, hindering further application of EEG in stroke rehabilitation training. One of the challenges is that the BCI involved rehabilitation process is unsatisfying. Three objectives were derived from this challenge. Firstly, at the beginning of all EEG related stroke rehabilitation training, it is both time and effort consuming to go through data collection and model training for every rehabilitation task. Therefore, in Chapter 4 of the thesis, the possibility of using an EEG model from one type of motor imagery (e.g.: elbow extension and flexion) to classify EEG from other types of motor imagery activities (e.g.: open a drawer) was investigated. Secondly, a novel training method was introduced together with a rehabilitation platform in Chapter 5. The results suggested that the proposed methods in this thesis are feasible and potentially effective. Thirdly, the transition of the offline analysis method to an online control method is one of the major factors that affect BCI performance. However, research particularly focused on the method of filtering the prediction of an online classification is scarce. In Chapter 6, two methods of filtering online classification predictions were proposed and evaluated in a pseudo-online classification paradigm, with the EEG data collected from Chapter 5. The other challenge is related to motor function assessments in rehabilitation. Motor function is generally assessed with standard questionnaire-based assessments. In these assessments, the rater requires the ratee to perform pre-defined movements and gives scores based on the quality of the movements. Therefore, those motor function assessments have inevitable subjective influences on the functional scores. In Chapter 7 of the thesis, the author investigated the possibility of using EEG data to assess motor function. As a preliminary investigation, EEG-based motor function assessments were only investigated for upper-extremity among participants with stroke. The results suggested that EEG data can be used to assess motor function accurately.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

The design of soft fluid filled actuators driven by conductive nylon

Author: 
Date created: 
2019-09-17
Abstract: 

Soft robots have become increasingly prevalent due to their distinct advantages over traditional rigid robots such as high deformability and good impact resistance. However, soft robotics are currently limited by bulky, non-portable methods of actuation. In this study, we propose a soft actuator driven by conductive nylon artificial muscles which is able to produce forces up to 1.2N. By utilizing nylon artificial muscles, the system does not require sizable pumps or compressors for actuation. The proposed actuator is made up of two main components, a sealed bladder filled with air and an arrangement of nylon artificial muscles. The quasi static behavior of the actuator is characterized using established hyper elastic models and validated against experimental results (maximum error of 5.3%). Using these models, a set of design considerations are formulated which outline the achievable torques for various actuator dimensions.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Audio-visual speech processing using deep learning techniques

Author: 
Date created: 
2019-10-02
Abstract: 

Speech separation is the task of segregating a target speech signal from background interference. To differentiate the separation of multiple speech sources from separating speech from non-speech noise, the terms Speaker Separation and Speech Enhancement (or Denoising) are commonly used, respectively. Speech separation can benefit from exploiting different modalities, i.e., audio and visual, and directional information when multiple microphones are available. A new approach is developed for subject-independent speaker separation by incorporating spectral, spatial and visual features. The audio signals have their magnitude and phase modified in the frequency domain for the speaker separation. The key idea is to estimate the target magnitudes from the audio with the Permutation Invariant Training (PIT) technique and then refine these estimates using both visual and spatial audio features. Specifically, visual features are matched to the corresponding audio, and spatial audio features are used as side information and shown to provide drastic improvement for magnitude and phase estimation in terms of output speech intelligibility, quality, and the separation performance. Visual information also provides improvements. Hence, both visual and spatial features are shown to be useful for speaker separation. A monaural speech enhancement model is also developed which incorporates both audio and visual information. In contrast to the audio-visual speaker separation model, the audio-visual speech enhancement model operates in time-domain. Hence, there is no need for a transformation and separate models for estimation of magnitude and phase spectra. According to the results of the objective evaluations, exploiting visual information for enhancement applications improves the performance in terms of both output quality and intelligibility.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Ivan Bajic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Developing a machine learning framework for 24-hour data analysis aimed at early detection of cardiac arrhythmias as a guiding tool for physicians

Author: 
Date created: 
2019-05-20
Abstract: 

Cardiovascular diseases (CVD), defined as a spectrum of disorders primarily impacting the heart and the circulatory system, account for a substantial fraction of worldwide morbidity and mortality. Electrocardiograms (ECGs) are routinely implemented in a patient’s diagnosis, both in hospitals and outpatient settings. They serve as one of the primary diagnostic tools as patients encounter medical personnel, particularly in suspected CVD. A cardiac holter monitor is a medical diagnostic device, connected to the patient via several conductive leads placed across the chest, and "worn" on a strap across the shoulder. A holter is applied to record continuous ECG data (typically 24 hours). With recently emerging applications of Machine Learning (ML) in data analysis techniques, the need for human expertise and potential human error could be minimized, and prediction accuracy optimized considerably. Hence, the objective of this research is to develop a machine learning approach to eventually aid physicians with their decisions as a powerful guiding/assisting tool to analyse the ECG information reported by holter monitors. Furthermore, we aim to develop a computer aided diagnostic system that can assist expert cardiologists by providing intelligent, cost effective, and time-saving diagnosis. In this thesis, we implement a deep learning-based solution to analyse readings of 24-holter monitors. In our proposed solution, we train neural networks to extract high-level features from temporal signal recordings of holter monitors. We present a supervised neural network framework to predict the physician’s final interpretations based on the holter recorded signals. The outputs of the network contain the likelihood of four possible scenarios of Normal and three types of arrhythmias. The high classification performance of the proposed methodology emphasizes the capability of this framework to be used as an assisting tool alongside the physicians to interpret holter reports.

Document type: 
Thesis
Senior supervisor: 
Andrew Rawicz
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Investigation on compressive devices based on dielectric elastomer actuators

Author: 
Date created: 
2019-11-20
Abstract: 

Dielectric elastomer actuators (DEAs) are an emerging technology from the larger class of artificial muscle actuators, showing interesting properties such as softness and large actuation strains. A DEA can be used to create various types of motions (such as planar, rotary, and bending) and contact forces (such as tension, biaxial compression, and bending moment). Due to its interesting properties, there are a growing number of studies on improving DEA applicability, reliability, and characteristics. Additionally, despite limited commercial use, there is a growing global push toward commercializing DEA in various sectors. In this thesis, in an effort to investigate practical applications of DEAs, their functionality is studied in applications where compressive forces are exerted by the actuator. Two different modalities in which DEAs could apply compressive forces to an encompassed object are identified. To narrow the focus of the thesis, a practical application is introduced and investigated for each modality. The first practical application, based on the first modality, introduces the use of DEAs as a compression bandage to improve blood circulation in the human leg. The proposed compression bandage could potentially enable a controlled variable compression around the lower leg. The second practical application, based on the second modality, proposes a novel gripper that uses DEAs as a mean to apply a soft touch on objects. The proposed gripper can apply up to 2N of grasping force to select objects. The gripper may be adopted as an end effectors for collaborative robotic arms, where a human operator is collaborating with the robotic arm to handle a delicate object. Typically, DEAs are actuated using high electrical voltages of several kilovolts. This operating voltage gives rise to a safety concern for practical applications where DEAs are sought to be operated in proximity to the human body. Since the two aforementioned applications require, or may require the operation of DEAs close to the human body, it is very important to study the electrical safety of DEAs and investigate methods to manage the risk of their high operating voltages. The last part of this thesis discusses the electrical safety of DEAs in scenarios where DEA circuitry is in contact with the human body, and shows that they can be safely used. Critical parameters for identification of the electrical safety of DEAs are also introduced to assist in designing safer DEAs for such applications.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.