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Showing 1 to 13 of 13 for “"Physiological time series"”.
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BioSignalML: An abstract model for physiological time-series data
… standard framework, BioSignalML, for describing physiological time-series data, in order to address some of the challenges created by the diverse range of formats used for biosignal storage and exchange. I argue that the main cause of difficulties for researchers, wanting to share and exchange …
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Filter-based multiscale entropy analysis of complex physiological time series
… used in analyzing the complexity of physiologic time series. In this thesis, we re-interpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce the {\it filter-based multiscale entropy} (FME) which filters a time …
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Synchronization, Variability, and Nonlinearity Analysis: Applications to Physiological Time Series
… nonlinearity analysis and their applications in physiological time series. For synchronization analysis, we explore both intensity and directionality of interaction. We propose a computational method to specify frequency ratio n : m, detect the moment when the frequency ratio changes, and also …
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Physiological time series retrieval and prediction with locality-sensitive hashing
The amount of time series data collected in the medical community has recently been exploding due to widespread affordable sensors and storage devices. However, while the massive repositories of such physiological time series data provide enormous opportunities for machine learning to make …
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Complexity Analysis of Physiological Time Series with Applications to Neonatal Sleep Electroencephalogram Signals
This thesis investigates the complexity in physiological time series with application to neonatal sleep electroencephalography (EEG) signals. Complexity analysis is applied to two clinical data sets of neonatal sleep Electroencephalography(EEG) time series, to uncover the evolution of signal …
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Information and generative deep learning with applications to medical time-series
Physiological time-series data are a valuable but under-utilised resource in intensive care medicine. These data are highly-structured and contain a wealth of information about the patient state, but can be very high-dimensional and difficult to interpret. Understanding temporal relationships …
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Generalizable neural network representations of patient state in the intensive care unit
… used to extract the important attributes of this time-series data without manual feature selection. In this work, we explore how learned encoded representations of physiological time-series and events time-series can be used to effectively predict outcomes on a variety of tasks. We compare the …
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PhysioMiner : a scalable cloud based framework for physiological waveform mining
… machine learning and analytics framework for physiological waveform mining. It is a scalable and flexible solution for researchers and practitioners to build predictive models from physiological time series data. It allows users to specify arbitrary features and conditions to train the model, …
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Robust and scalable unsupervised learning via landmark diffusion, from theory to medical application
<p>Biomedical time series contain rich information about human systems, however, effective algorithms for analyzing long-term physiological time series have not yet been developed because of the huge volume size, high dimensionality and large noise nature of the data. Motivated by such challenging …
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Inference of the Novel Coronavirus 2019 in Patients fitted with Boston Scientific Medical Hardware
… hardware. By utilizing Boston Scientific’s physiological time series data from Heart Failure therapy devices such as pacemakers, we aim to determine if an algorithm can be built to anticipate worsening COVID-19 symptoms in real time in patients and therefore provide them with better …
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Advanced Robust Statistical Learning Methods with Application in Healthcare and Manufacturing
… heterogeneity, focusing on heterogeneous physiological time series data derived from Electronic Health Records, electrocardiograms, electroencephalograms and etc. Different factors in the latent factor model represent different characteristics of the time series. These latent factors are …
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Quantification of long-range power law correlations among healthy and pathologic subjects using detrended fluctuation analysis and multifractal detrended fluctuation analysis
… away from equilibrium. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this long-range correlation behavior. Two different non-linear dynamic methods namely Detrended Fluctuation Analysis (DFA) and Multifractal (MF) DFA are used for the …
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Multivariate sequential contrast pattern mining and prediction models for critical care clinical informatics
… fresh insights into disease dynamics over long time scales. In this research, we focus on the extraction of computational physiological markers, in the form of relevant medical episodes, event sequences and distinguishing sequential patterns. These interesting patterns known as sequential …