The Promise of Stochastic Resonance in Falls Prevention

Multisensory integration is essential for maintenance of motor and cognitive abilities, thereby ensuring normal function and personal autonomy. Balance control is challenged during senescence or in motor disorders, leading to potential falls. Increased uncertainty in sensory signals is caused by a number of factors including noise, defined as a random and persistent disturbance that reduces the clarity of information. Counter-intuitively, noise can be beneficial in some conditions. Stochastic resonance is a mechanism whereby a particular level of noise actually enhances the response of non-linear systems to weak sensory signals. Here we review the effects of stochastic resonance on sensory modalities and systems directly involved in balance control. We highlight its potential for improving sensorimotor performance as well as cognitive and autonomic functions. These promising results demonstrate that stochastic resonance represents a flexible and non-invasive technique that can be applied to different modalities simultaneously. Finally we point out its benefits for a variety of scenarios including in ambulant elderly, skilled movements, sports and to patients with sensorimotor or autonomic dysfunctions.Multisensory integration is essential for maintenance of motor and cognitive abilities, thereby ensuring normal function and personal autonomy. Balance control is challenged during senescence or in motor disorders, leading to potential falls. Increased uncertainty in sensory signals is caused by a number of factors including noise, defined as a random and persistent disturbance that reduces the clarity of information. Counter-intuitively, noise can be beneficial in some conditions. Stochastic resonance is a mechanism whereby a particular level of noise actually enhances the response of non-linear systems to weak sensory signals. Here we review the effects of stochastic resonance on sensory modalities and systems directly involved in balance control. We highlight its potential for improving sensorimotor performance as well as cognitive and autonomic functions. These promising results demonstrate that stochastic resonance represents a flexible and non-invasive technique that can be applied to different modalities simultaneously. Finally we point out its benefits for a variety of scenarios including in ambulant elderly, skilled movements, sports and to patients with sensorimotor or autonomic dysfunctions

Oscillations in microvascular flow:their relationship to tissue oxygenation, cellular metabolic function and their diagnostic potential for detecting skin melanoma - clinical, experimental and theoretical investigations

Tumour vasculature is known to be inefficient and abnormal due to poorly regulated angiogenesis during tumour growth. This leads to irregular patterns of blood flow which are spatially and temporally heterogeneous. Many investigations into the characteristics of tumours are invasive and performed on animal models. However, continuous technological and theoretical advancement is leading to the use of non-invasive imaging techniques, providing in vivo information on humans. Here, data recorded using laser Doppler flowmetry (LDF) in malignant melanoma and control lesions are analysed using techniques designed for application to non-stationary, time-varying data. Many studies utilising LDF have previously revealed increased blood flow in malignant lesions, but very little attention has been paid to the dynamics of this blood flow, or how it changes over time. As it has been demonstrated previously that the oscillations observed within blood flow data are physiologically significant, failure to extract these characteristics loses information about the underlying dynamical system from which the blood flow data were recorded. Significant differences in blood flow dynamics are revealed and used in the development of a diagnostic test for melanoma. In addition to the characterization of the blood flow dynamics in melanoma, possible causes for the observed changes are investigated and related to two widely observed characteristics of cancer, intermittent hypoxia and altered cellular energy metabolism. The former is explored through the analysis of blood flow and oxygenation data recorded during dry static apnoea, whilst the latter is modelled using coupled phase oscillators

Computational modelling of normal function and pathology in neural systems: new tools, techniques and results in cortex and basal ganglia.

Oscillations between various populations of neurons are common and well documented. However, there are oscillations that can emerge within networks of neurons that are pathological and highly detrimental to the normal functioning of the brain. This thesis is concerned with modelling the transition from healthy network states to the pathological oscillatory states in two different brain disorders; epilepsy and Parkinson’s disease (PD). To study these transitions, existing computational methods for modelling large systems of interacting populations of neurons are used and new tools are developed. The first half of this thesis explores the evidence for the dynamic evolution of focal epilepsy using bifurcation analysis of a neural mass model, and relating these bifurcations to specific features of clinical data recordings in the time-domain. These findings are used to map out the evolution of seizures based on features of segments of the clinically recorded electroencephalograms. The similarity of seizure evolution within patients is tested. Statistically significant similarities were found between the evolutions of seizures from the same patient. In the latter half of the thesis a way of creating firing rate models is described, in which the value of the membrane time constant is dependent on the activity of afferent populations. This method is applied to modelling the basal ganglia (BG). The hypothesis that the BG are responsible for selection in the primate brain is tested and confirmed. The model is then used to investigate the development of PD. It was found that the loss of dopaminergic innervation caused a failure of selection capability but did not directly give rise to the beta oscillations ubiquitous in PD. Network connection strength changes that are seen in PD cause the model to regain selection functionality but lead to a beta frequency resting state oscillation, as is the case in real PD

Identification of Chronic Postural Stability Impairments Associated With History of Concussion

Concussion is the most common form of traumatic brain injury (TBI). However, there is a disproportionate level of understanding between the acute and chronic impairments associated with traumatic brain injury. Specifically, problems maintaining balance during standing and walking are cardinal signs of acute concussion, but the temporal extent to which postural control deficits remain following the initial injury are not well defined or understood. The purpose of the projects composing this dissertation was to examine the long-term effects of a prior history of concussion on static (i.e. standing) and dynamic (i.e. gait) postural control. To address this, healthy adults aged 18-45 reporting a prior history of concussion(s) as well as age-matched controls with no documented concussion history were recruited to participate. Static postural control was assessed using a force plate system to track each participant’s center-of-pressure during standing. Spatiotemporal parameters as well as head stability during gait were assessed using a pressure-sensitive walkway and accelerometers placed at the head, neck, and lower trunk, respectively. The findings of these projects indicate that concussion has detrimental effects on both static and dynamic postural stability years after the initial injury and clinical determination of recovery. Specifically, individuals with a prior history of concussion demonstrated greater postural sway displacement and reduced sway regularity under dual-task conditions compared to the control group. In addition, previously concussed individuals demonstrated less variability in their gait cadence and step length, which suggests a reduction in the complexity of the neural networks contributing to postural control. Lastly, individuals with a history of concussion demonstrated greater triaxial accelerations at the head during gait, indicating a reduced ability to attenuate gait-related oscillations and stabilize the head. Collectively, these findings indicate that concussion is associated with impaired postural control that persists for years after the initial injury and well beyond the point where clinical testing protocols can identify deficits in maintaining balance. Future efforts should be directed toward incorporating more sophisticated measures and analyses of postural stability in concussion screenings to improve clinicians’ abilities to identify the scope in which concussion negatively impacts the function of the central nervous system

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards

Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness and non-stationary characteristics make them impossible to be described in an accurate way with a simple technique, requiring analysis and characterization involves techniques that take into account their non-stationarity. For that, new advanced techniques in order to improve the efficiency of the EEG based methods used in the clinical practice have to be developed. The main objective of this thesis was to investigate and implement different methods based on nonlinear techniques in order to develop indexes able to characterize the frequency spectrum, the nonlinear dynamics and the complexity of the EEG signals recorded in different state of consciousness. Firstly, a new method for removing peak and spike in biological signal based on the signal envelope was successfully designed and applied to simulated and real EEG signals, obtaining performances significantly better than the traditional adaptive filters. Then, several studies were carried out in order to extract and evaluate EEG measures based on nonlinear techniques in different contexts such as the automatic detection of sleepiness and the characterization and prediction of the nociceptive stimuli and the assessment of the sedation level. Four novel indexes were defined by calculating entropy of the Choi-Williams distribution (CWD) with respect to time or frequency, by using the probability mass function at each time instant taken independently or by using the probability mass function of the entire CWD. The values of these indexes tend to decrease, with different proportion, when the behavior of the signals evolved from chaos or randomness to periodicity and present differences when comparing EEG recorded in eyes-open and eyes-closed states and in ictal and non-ictal states. Measures obtained with time-frequency representation, mutual information function and correntropy, were applied to EEG signals for the automatic sleepiness detection in patients suffering sleep disorders. The group of patients with excessive daytime sleepiness presented more power in ¿ band than the group without sleepiness, which presented higher spectral and cross-spectral entropy in the frontal zone in d band. More complexity in the occipital zone was found in the group of patients without sleepiness in ß band, while a stronger nonlinear coupling between the occipital and frontal regions was detected in patients with excessive daytime sleepiness, in ß band. Time-frequency representation and non-linear measures were also used in order to study how adaptation and fatigue affect the event-related brain potentials to stimuli of different modalities. Differences between the responses to infrequent and frequent stimulation in different recording periods were found in series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation. Nonlinear measures calculated on EEG filtered in the traditional frequency bands and in higher frequency bands improved the assessment of the sedation level. These measures were obtained by applying all the developed techniques on signals recorded from patients sedated, in order to predict the responses to pain stimulation such as nail bad compression and endoscopy tube insertion. The proposed measures exhibit better performances than the bispectral index (BIS), a traditional indexes used for hypnosis assessment. In conclusion, nonlinear measures based on time-frequency representation, mutual information functions and correntropy provided additional information that helped to improve the automatic sleepiness detection, the characterization and prediction of the nociceptive responses and thus the assessment of the sedation level.El registro de la señal Electroencefalografíca (EEG) proporciona información sobre los cambios en la actividad cerebral asociados con varios estados de la anestesia, la epilepsia, la atención cerebral, los trastornos del sueño, los trastornos cerebrales, etc. Los EEG son señales complejas cuyas propiedades estadísticas dependen del espacio y del tiempo. Sus características aleatorias y no estacionarias hacen imposible que el EEG se describa de forma precisa con una técnica sencilla requiriendo un análisis y una caracterización que implica técnicas que tengan en cuenta su no estacionariedad. Todo esto aumenta la necesidad de desarrollar nuevas técnicas avanzadas con el fin de mejorar la eficiencia de los métodos utilizados en la práctica clínica que son basados en el análisis de EEG. En esta tesis se han investigado y aplicado diferentes métodos utilizando técnicas no lineales con el fin de desarrollar índices capaces de caracterizar el espectro de frecuencias, la dinámica no lineal y la complejidad de las señales EEG registradas en diferentes estados de conciencia. En primer lugar, se ha desarrollado un nuevo algoritmo basado en la envolvente de la señal para la eliminación de ruido de picos en las señales biológicas. Este algoritmo ha sido aplicado a señales simuladas y reales obteniendo resultados significativamente mejores comparados con los filtros adaptativos tradicionales. Seguidamente, se han llevado a cabo varios estudios con el fin de extraer y evaluar las medidas de EEG basadas en técnicas no lineales en diferentes contextos. Se han definido nuevos índices mediante el cálculo de la entropía de la distribución de Choi-Williams (DCW) con respecto al tiempo o la frecuencia. Se ha observado que los valores de estos índices tienden a disminuir, en diferentes proporciones, cuando el comportamiento de las señales evoluciona de caótico o aleatorio a periódico. Además, se han encontrado valores diferentes de estos índices aplicados a la señal EEG registrada en diferentes estados. Diferentes medidas basadas en la representación tiempo-frecuencia, la función de información mutua y la correntropia se han aplicado al EEG para la detección automática de la somnolencia en pacientes que sufren trastornos del sueño. Se ha observado en la zona frontal que la potencia en la banda θ es mayor en los pacientes con somnolencia diurna excesiva, mientras que la entropía espectral y la entropía espectral cruzada en la banda δ es mayor en los pacientes sin somnolencia. En el grupo sin somnolencia se ha encontrado más complejidad en la zona occipital, mientras que el acoplamiento no lineal entre las regiones occipital y frontal ha resultado más fuerte en pacientes con somnolencia diurna excesiva, en la banda β. La representación tiempo-frecuencia y las medidas no lineales se han utilizado para estudiar cómo la adaptación y la fatiga afectan a los potenciales cerebrales relacionados con estímulos térmicos, eléctricos y auditivos. Analizando el promedio de varias épocas de EEG grabadas después de la estimulación, se han encontrado diferencias entre las respuestas a la estimulación frecuente e infrecuente en diferentes períodos de registro. Todas las técnicas que se han desarrollado, se han aplicado a señales EEG registradas en pacientes sedados, con el fin de predecir las respuestas a la estimulación del dolor. Un conjunto de medidas calculadas en señales EEG filtradas en diferentes bandas de frecuencia ha permitido mejorar la evaluación del nivel de sedación. Las medidas propuestas han presentado un mejor rendimiento comparado con el índice bispectral, un indicador de hipnosis tradicional. En conclusión, las medidas no lineales basadas en la representación tiempofrecuencia, funciones de información mutua y correntropia han proporcionado informaciones adicionales que contribuyeron a mejorar la detección automática de la somnolencia, la caracterización y predicción de las respuestas nociceptivas y por lo tanto la evaluación del nivel de sedación

Age-Related Differences in Motor Performance

The purpose of this work was to study the age effects on average performance and variability of movement responses in children, young adults, and older adults across multiple motor tasks. Optimal motor performance is observed in healthy young adults with declines observed at either end of the lifespan. This pattern has been represented as a U-shaped/inverted U-shaped curve. Little is known about if this pattern persists in chewing dynamics. While chewing has been found to improve aspects of attention, a cognitive function, research is limited on the relationship between chewing and other motor tasks. The first aim of this research was to conduct a scoping systematic review to identify what measures of variability are reported for preferred performance of chewing and walking in children, young adults, and older adults and the age-related differences across these age groups. The available research was insufficient across these groups and does not support the perspective that children and older adults are more variable than young adults. The second aim was to examine age-related differences in averages and variability of chewing, reaction time, balance, and walking responses across children, young adults, and older adults. A U-shaped curve was revealed for reaction time and postural sway with the young adults producing faster reaction times and decreased postural sway than the children and older adults. Chewing rates followed a similar curve but with children chewing at faster rates than young and older adults. No age-related differences were observed for normalized gait speed. The final aim was to examine dual task relationships between chewing and secondary motor tasks in children. Sixteen healthy children completed finger tapping, reaction time, and walking while chewing at different speeds. Chewing rates varied when produced with a secondary motor task and the secondary motor tasks were differentially influenced by chewing. Reaction times slowed during chewing while walking rates increased/decreased with changes in chewing rates. This relationship was not as strong as previous reports in adults. Overall, the anticipated patterns across the age groups were only partially revealed within this work. Understanding normal movement patterns is the foundation to identifying variations in atypical populations