DEEP LEARNING AND PHYSIOLOGY BASED APPROACH TO DERIVE AND LINK OBSTRUCTIVE SLEEP APNOEA PHENOTYPES AND SYMPTOMATOLOGY
Funder
National Health and Medical Research Council
Funding Amount
$402,978.00
Summary
Obstructive sleep apnoea (OSA) is a highly prevalent nocturnal breathing disorder strongly related to daytime sleepiness, accident risk and reduced quality of life. However, the current severity index, the apnoea-hypopnoea index, poorly predicts daytime sleepiness and vigilance. In this project we elegantly combine physiological insight and artificial intelligence to develop and evaluate novel clinically applicable computational tools for detailed quantification of OSA severity and its symptoms.
Spontaneous activity and neural decoding in the developing brain. This project aims to investigate how patterns of neural activity emerge in the developing brain, using the zebrafish as a model system. This project expects to generate new knowledge regarding the functional significance of spontaneously generated activity, and how it interacts with sensory experience. The expected outcomes of this project include enhanced capacity at the interface between neuroscience and computation. This should ....Spontaneous activity and neural decoding in the developing brain. This project aims to investigate how patterns of neural activity emerge in the developing brain, using the zebrafish as a model system. This project expects to generate new knowledge regarding the functional significance of spontaneously generated activity, and how it interacts with sensory experience. The expected outcomes of this project include enhanced capacity at the interface between neuroscience and computation. This should provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing.Read moreRead less
How does environmental enrichment affect brain development? This project aims to use brain imaging and advanced computational analyses to investigate how early sensory experience affects brain development. It adopts the larval zebrafish as a model system, since they display sophisticated behaviours from an early age, and neural activity can be recorded at whole-brain scale with single neuron resolution. The project aims to generate new knowledge regarding environmental effects on brain developme ....How does environmental enrichment affect brain development? This project aims to use brain imaging and advanced computational analyses to investigate how early sensory experience affects brain development. It adopts the larval zebrafish as a model system, since they display sophisticated behaviours from an early age, and neural activity can be recorded at whole-brain scale with single neuron resolution. The project aims to generate new knowledge regarding environmental effects on brain development and behaviour. This will provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing. The expected outcomes also include enhanced capacity at the interface between neuroscience and computation.Read moreRead less
How do patterns of brain activity emerge during early life? This project uses theory and experiment to investigate how neural coding emerges in the developing brain. It adopts the larval zebrafish as a model system, because neural activity can be recorded at whole-brain scale but with single neuron resolution. The project expects to generate new knowledge regarding how neural activity comes to represent sensory stimuli, and new statistical models for interpreting large-scale patterns of neural a ....How do patterns of brain activity emerge during early life? This project uses theory and experiment to investigate how neural coding emerges in the developing brain. It adopts the larval zebrafish as a model system, because neural activity can be recorded at whole-brain scale but with single neuron resolution. The project expects to generate new knowledge regarding how neural activity comes to represent sensory stimuli, and new statistical models for interpreting large-scale patterns of neural activity. This will provide significant benefits including greater insight into normal brain development, and the formulation of new concepts potentially relevant for brain-inspired computing. The expected outcomes also include enhanced capacity at the interface between neuroscience and computation.Read moreRead less
A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algor ....A Novel Framework for Optimised Ensemble Classifier. The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.Read moreRead less
Mechanisms of nerve fibre guidance by molecular gradients. Brain wiring is crucial for brain function. The project will investigate the basic principles underlying the development of brain wiring, using both experiments and mathematical models. This will lead a predictive model of how wiring develops, both in normal and abnormal situations.
An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed all ....An automated system for the analysis of road safety and conditions. This project aims to develop an automated system for the analysis of road safety and conditions. Digital video road data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project will develop deep learning based neural network techniques which can learn and classify roadside objects so that video data can be automatically analysed allowing the estimation of the proximity of objects for road safety and rating. The expected outcome will be new identification techniques and software which can be incorporated with road data collection systems.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Application of artificial neural network in flood emergency decision support system. This project will develop a method for rapid estimation of flood water levels. This will increase the warning time for flood evacuation in small coastal catchments where traditional estimating techniques are too time-consuming.