49 research outputs found
Investigating metabolomic biomarkers of hypoxic ischaemic encephalopathy
Background An early objective biomarker to predict the severity of hypoxic-ischaemic encephalopathy (HIE) and identify infants suitable for intervention remains elusive. This thesis aims to progress metabolomic markers of HIE through a pipeline of biomarker discovery and validation by employing a novel untargeted mass spectrometry metabolomic method. Methodology Term infants with perinatal asphyxia were recruited, all having umbilical cord blood (UCB) drawn and biobanked within three hours of birth. HIE was defined by Sarnat score at 24hours and continuous multichannel-EEG. Infant neurodevelopment was assessed at 36-42 months using the Bayley Scales of Infant and Toddler Development Ed. III (BSID-III). Untargeted metabolomic analysis of UCB was performed using direct injection FT-ICR mass spectrometry (DI FT-ICR MS). Putative metabolite annotations and lipid classes were assigned and pathway analysis was performed. Results Untargeted metabolomic analysis: Thirty enrolled infants were diagnosed with HIE, including 17 mild, 8 moderate, and 5 severe cases. Pathway analysis revealed that ΔHIE was associated with a 50% and 75% perturbation of tryptophan and pyrimidine metabolism respectively, alongside alterations in amino acid pathways. Significant metabolite alterations were detected from six putatively identified lipid classes including fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids and prenol lipids. Outcome prediction: Metabolite model scores significantly correlated with outcome R=0.429 (model A) and R=0.549 (model B) respectively. Model B demonstrates the potential to predict both severe outcome (AUROC of 0.915) and intact survival (AUROC of 0.800). The effect of haemolysis: On average 5% of polar and 1.5% of non-polar features were altered between paired haemolysed and clean samples. However unsupervised multivariate analysis concluded that the preanalytical variability introduced by haemolysis was negligible compared with the inherent biological inter-individual variability. Conclusion This research has employed untargeted metabolomics to identify potential early cord blood biomarkers of HIE and has performed the technical validation of previously proposed markers
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Near-Infrared Spectroscopy for Brain Computer Interfacing
A brain-computer interface (BCI) gives those suffering from neuromuscular
impairments a means to interact and communicate with their surrounding
environment. A BCI translates physiological signals, typically electrical,
detected from the brain to control an output device. A significant problem with
current BCIs is the lengthy training periods involved for proficient usage, which
can often lead to frustration and anxiety on the part of the user and may even lead
to abandonment of the device. A more suitable and usable interface is needed to
measure cognitive function more directly. In order to do this, new measurement
modalities, signal acquisition and processing, and translation algorithms need to
be addressed. This work implements a novel approach to BCI design, using noninvasive
near-infrared spectroscopic (NIRS) techniques to develop a userfriendly
optical BCI. NIRS is a practical non-invasive optical technique that can
detect characteristic haemodynamic responses relating to neural activity. This
thesis describes the use of NIRS to develop an accessible BCI system requiring
very little user training. In harnessing the optical signal for BCI control an
assessment of NIRS signal characteristics is carried out and detectable
physiological effects are identified for BCI development. The investigations into
various mental tasks for controlling the BCI show that motor imagery functions
can be detected using NIRS. The optical BCI (OBCI) system operates in realtime
characterising the occurrence of motor imagery functions, allowing users to
control a switch - a “Mindswitch”. This work demonstrates the great potential of
optical imaging methods for BCI development and brings to light an innovative
approach to this field of research
Human-Centric Machine Vision
Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans
Fear Classification using Affective Computing with Physiological Information and Smart-Wearables
Mención Internacional en el título de doctorAmong the 17 Sustainable Development Goals proposed within the 2030 Agenda
and adopted by all of the United Nations member states, the fifth SDG is a call
for action to effectively turn gender equality into a fundamental human right and
an essential foundation for a better world. It includes the eradication of all types
of violence against women. Focusing on the technological perspective, the range of
available solutions intended to prevent this social problem is very limited. Moreover,
most of the solutions are based on a panic button approach, leaving aside
the usage and integration of current state-of-the-art technologies, such as the Internet
of Things (IoT), affective computing, cyber-physical systems, and smart-sensors.
Thus, the main purpose of this research is to provide new insight into the design and
development of tools to prevent and combat Gender-based Violence risky situations
and, even, aggressions, from a technological perspective, but without leaving aside
the different sociological considerations directly related to the problem. To achieve
such an objective, we rely on the application of affective computing from a realist
point of view, i.e. targeting the generation of systems and tools capable of being implemented
and used nowadays or within an achievable time-frame. This pragmatic
vision is channelled through: 1) an exhaustive study of the existing technological
tools and mechanisms oriented to the fight Gender-based Violence, 2) the proposal
of a new smart-wearable system intended to deal with some of the current technological
encountered limitations, 3) a novel fear-related emotion classification approach
to disentangle the relation between emotions and physiology, and 4) the definition
and release of a new multi-modal dataset for emotion recognition in women.
Firstly, different fear classification systems using a reduced set of physiological signals are explored and designed. This is done by employing open datasets together
with the combination of time, frequency and non-linear domain techniques. This
design process is encompassed by trade-offs between both physiological considerations
and embedded capabilities. The latter is of paramount importance due to
the edge-computing focus of this research. Two results are highlighted in this first
task, the designed fear classification system that employed the DEAP dataset data
and achieved an AUC of 81.60% and a Gmean of 81.55% on average for a subjectindependent
approach, and only two physiological signals; and the designed fear
classification system that employed the MAHNOB dataset data achieving an AUC
of 86.00% and a Gmean of 73.78% on average for a subject-independent approach,
only three physiological signals, and a Leave-One-Subject-Out configuration. A detailed
comparison with other emotion recognition systems proposed in the literature
is presented, which proves that the obtained metrics are in line with the state-ofthe-
art.
Secondly, Bindi is presented. This is an end-to-end autonomous multimodal system
leveraging affective IoT throughout auditory and physiological commercial off-theshelf
smart-sensors, hierarchical multisensorial fusion, and secured server architecture
to combat Gender-based Violence by automatically detecting risky situations
based on a multimodal intelligence engine and then triggering a protection protocol.
Specifically, this research is focused onto the hardware and software design of one of
the two edge-computing devices within Bindi. This is a bracelet integrating three
physiological sensors, actuators, power monitoring integrated chips, and a System-
On-Chip with wireless capabilities. Within this context, different embedded design
space explorations are presented: embedded filtering evaluation, online physiological
signal quality assessment, feature extraction, and power consumption analysis.
The reported results in all these processes are successfully validated and, for some
of them, even compared against physiological standard measurement equipment.
Amongst the different obtained results regarding the embedded design and implementation
within the bracelet of Bindi, it should be highlighted that its low power
consumption provides a battery life to be approximately 40 hours when using a 500
mAh battery.
Finally, the particularities of our use case and the scarcity of open multimodal datasets dealing with emotional immersive technology, labelling methodology considering
the gender perspective, balanced stimuli distribution regarding the target
emotions, and recovery processes based on the physiological signals of the volunteers
to quantify and isolate the emotional activation between stimuli, led us to the definition
and elaboration of Women and Emotion Multi-modal Affective Computing
(WEMAC) dataset. This is a multimodal dataset in which 104 women who never
experienced Gender-based Violence that performed different emotion-related stimuli
visualisations in a laboratory environment. The previous fear binary classification
systems were improved and applied to this novel multimodal dataset. For instance,
the proposed multimodal fear recognition system using this dataset reports up to
60.20% and 67.59% for ACC and F1-score, respectively. These values represent a
competitive result in comparison with the state-of-the-art that deal with similar
multi-modal use cases.
In general, this PhD thesis has opened a new research line within the research group
under which it has been developed. Moreover, this work has established a solid base
from which to expand knowledge and continue research targeting the generation of
both mechanisms to help vulnerable groups and socially oriented technology.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: David Atienza Alonso.- Secretaria: Susana Patón Álvarez.- Vocal: Eduardo de la Torre Arnan
Cellular and molecular investigations of undiagnosed neurometabolic disorders
Inborn errors of metabolism (IEM) affect 1 in 500 newborns causing significant disease-burden and mortality throughout childhood. However, despite extensive genetic and biochemical investigations the cause of disease remains unknown in up to 50% of patients with neurological symptoms; so-called neurometabolic disorders (NMD). The overarching aim of this thesis was to determine the cellular and molecular aetiologies for the clinical phenotypes seen in patients with undiagnosed NMD. In order to improve the diagnosis of these disorders in clinical practice, a comprehensive targeted gene panel of 614 genes known to cause IEM was designed and a cohort of 44 patients was analysed. A definitive or probable genetic diagnosis was achieved in 53% of patients without a prior genetic diagnosis. Method optimisation and validation, comparison to other diagnostic strategies and the advantages and disadvantages of targeted sequencing are reviewed. Case reports, novel mutations/phenotypes and their contribution to the expansion of the literature are described. Whole exome sequencing and functional characterisation was also undertaken for patients who had been extensively clinically investigated previously. Five patients identified with mutations in the mitochondrial glutamate transporter, SLC25A22, presenting with novel biochemical phenotypes are described and novel transporter functions are postulated. One patient diagnosed with a potassium channelopathy with biochemical abnormalities and anticonvulsant responses suggestive of an inborn error of vitmain B6 metabolism is documented and the mechanisms underlying the generalised anticonvulsant effects of vitamin B6 are postulated. Characterisation of a possible novel inborn error of lysine metabolism in a patient presenting with hyperlysinaemia and motor neuron disease is also discussed. These studies also demonstrate the complexity of unravelling the relationship between genotype and phenotype and highlight the need for novel functional assays to assess the pathogenicity of sequence variants. Mass spectrometry-based assays were developed to enable characterisation of disorders affecting vitamin B6 homeostasis, including pyridox(am)ine 5'-phosphate oxidase (PNPO), antiquitin and PROSC deficiency, the latter being a novel disorder. The differences between pyridoxine- and pyridoxal phosphate-responsive PNPO deficiency and fibroblast vitamer profiles in all patients were all investigated. Finally, multiple methodologies were employed with the aim of understanding the biological function of PROSC