1,588 research outputs found
Automating the Calibration of a Neonatal Condition Monitoring System
Abstract. Condition monitoring of premature babies in intensive care can be carried out using a Factorial Switching Linear Dynamical System (FSLDS) [15]. A crucial part of training the FSLDS is the manual calibration stage, where an interval of normality must be identified for each baby that is monitored. In this paper we replace this manual step by using a classifier to predict whether an interval is normal or not. We show that the monitoring results obtained using automated calibration are almost as good as those using manual calibration
Online Discrimination of Nonlinear Dynamics with Switching Differential Equations
How to recognise whether an observed person walks or runs? We consider a
dynamic environment where observations (e.g. the posture of a person) are
caused by different dynamic processes (walking or running) which are active one
at a time and which may transition from one to another at any time. For this
setup, switching dynamic models have been suggested previously, mostly, for
linear and nonlinear dynamics in discrete time. Motivated by basic principles
of computations in the brain (dynamic, internal models) we suggest a model for
switching nonlinear differential equations. The switching process in the model
is implemented by a Hopfield network and we use parametric dynamic movement
primitives to represent arbitrary rhythmic motions. The model generates
observed dynamics by linearly interpolating the primitives weighted by the
switching variables and it is constructed such that standard filtering
algorithms can be applied. In two experiments with synthetic planar motion and
a human motion capture data set we show that inference with the unscented
Kalman filter can successfully discriminate several dynamic processes online
Detecting dynamical changes in vital signs using switching Kalman filter
Vital signs contain valuable information about the health condition of patients during their stay in the ward, when deterioration process begins. The use of methods to predict and detect regime changes such as switching models can help to understand how vital sign dynamics are altered in health and disease. However, time series of vital signs are remarkably non-stationary in these scenarios. The objective of this study is to quantify the potential bias of the switching models in the presence of non-stationary time series, when the inputs are spectral, symbolic and entropy indices. To distinguish stationary periods from non-stationary, a stationarity test was used to verify the stability of the mean and variance over short periods. Then, we compared the results from a switching Kalman filter (SKF) model trained using only indices obtained over stationary periods, with a model trained using indices obtained solely over non-stationary periods. It was observed that the indices measured over stationary and non-stationary periods were significantly different. The results were highly dependent of what indices were used as input, being the multiscale entropy (MSE) the most efficient approach, achieving an average correlation coefficients of 38%
Detecting dynamical changes in vital signs using switching Kalman filter
Vital signs contain valuable information about patients' health status during their stay in general wards, when the deterioration process begins. The use of methods to predict and detect regime changes such as switching models can help to understand how vital sign dynamics are altered in disease conditions. However, time series of vital signs are remarkably non-stationary in these scenarios. The objective of this study is to quantify the potential bias of switching models in the presence of non-stationarities, when the inputs are spectral, symbolic and entropy indices. To distinguish stationary from non-stationary periods, a test was used to verify the stability of the mean and variance over short periods. Then, we compared the results from a switching Kalman filter (SKF) model trained using indices obtained over stationary periods with a model trained solely over non-stationary periods. It was observed that indices measured over stationary and non-stationary periods were significantly different. The results of switching models were highly dependent on the indices that were used as inputs. The multi-scale entropy (MSE) approach presented the highest correlation values between non-stationary and stationary switches, an average correlation coefficient of 38%
Bayesian Condition Monitoring in Neonatal Intensive Care
Institute for Adaptive and Neural ComputationThe observed physiological dynamics of an infant receiving intensive care contain a great deal of information about factors which cannot be examined directly, including the state of health of the infant and the operation of the monitoring equipment. This type of data tends to contain both common, recognisable patterns (e.g. as caused by certain clinical operations or artifacts) and some which are rare and harder to interpret. The problem of identifying the presence of these patterns using prior knowledge is clinically significant, and one which is naturally described in terms of statistical machine learning.
In this thesis I develop probabilistic dynamical models which are capable of making useful inferences from neonatal intensive care unit monitoring data. The Factorial Switching Kalman Filter (FSKF) in particular is adopted as a suitable framework for monitoring the condition of an infant. The main contributions are as follows: (1) the application of the FSKF for inferring common factors in physiological monitoring data, which includes finding parameterisations of linear dynamical models to represent common physiological and artifactual conditions, and adapting parameter estimation and inference techniques for the purpose; (2) the formulation of a model for novel physiological dynamics, used to infer the times in which something is happening which is not described by any of the known patterns. EM updates are derived for the latter model in order to estimate parameters. Experimental results are given which show the developed methods to be effective on genuine monitoring data
Dynamical models for neonatal intensive care monitoring
The vital signs monitoring data of an infant receiving intensive care are a rich source of information
about its health condition. One major concern about the state of health of such patients
is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle
and current diagnosis procedures involve slow laboratory testing, sepsis detection based on
the monitored physiological dynamics is a clinically significant task. This challenging problem
can be thoroughly modelled as real-time inference within a machine learning framework.
In this thesis, we develop probabilistic dynamical models centred around the goal of providing
useful predictions about the onset of neonatal sepsis. This research is characterised by
the careful incorporation of domain knowledge for the purpose of extracting the infantâs true
physiology from the monitoring data.
We make two main contributions. The first one is the formulation of sepsis detection as
learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model
investigates the extent to which physiological events observed in the patientâs monitoring traces
could be used for the early detection of neonatal sepsis. In addition, the proposed approach
involves exact marginalisation over missing data at inference time. When applying the ARHMM
on a real-world dataset, we found that it can produce effective predictions about the
onset of sepsis.
Second, both sepsis and clinical event detection are formulated as learning and inference in
a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical
systems where complex interactions between modes of operation can be represented as a twolevel
hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer
models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis.
The model parameterisation and estimation procedures are adapted to the specifics of
physiological monitoring data. We demonstrate that the performance of the HSLDS for the
detection of sepsis is not statistically different from the AR-HMM, despite the fact that the
latter model is given âground truthâ annotations of the patientâs physiology
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