Showing papers by "Lionel Tarassenko published in 2006"

Integrated monitoring and analysis for early warning of patient deterioration

Abstract: Recently there has been an upsurge of interest in strategies for detecting at-risk patients in order to trigger the timely intervention of a Medical Emergency Team (MET), also known as a Rapid Response Team (RRT). We review a real-time automated system, BioSign, which tracks patient status by combining information from vital signs monitored non-invasively on the general ward. BioSign fuses the vital signs in order to produce a single-parameter representation of patient status, the Patient Status Index. The data fusion method adopted in BioSign is a probabilistic model of normality in five dimensions, previously learnt from the vital sign data acquired from a representative sample of patients. BioSign alerts occur either when a single vital sign deviates by close to ±3 standard deviations from its normal value or when two or more vital signs depart from normality, but by a smaller amount. In a trial with high-risk elective/emergency surgery or medical patients, BioSign alerts were generated, on average, every 8 hours; 95% of these were classified as ‘True' by clinical experts. Retrospective analysis has also shown that the data fusion algorithm in BioSign is capable of detecting critical events in advance of single-channel alerts.

Application of independent component analysis in removing artefacts from the electrocardiogram

Abstract: Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using independent component analysis (ICA). An ICA algorithm is tested on three-channel ECG recordings taken from human subjects, mostly in the coronary care unit. Results are presented that show that ICA can detect and remove a variety of noise and artefact sources in these ECGs. One difficulty with the application of ICA is the determination of the order of the independent components. A new technique based on simple statistical parameters is proposed to solve this problem in this application. The developed technique is successfully applied to the ECG data and offers potential for online processing of ECG using ICA.

A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients.

Abstract: We conducted a randomised controlled trial of mandated five-channel physiological monitoring vs standard care, in acute medical and surgical wards in a single UK teaching hospital. In all, 402 high-risk medical and surgical patients were studied. The primary outcome was the proportion of patients experiencing one or more major adverse events, including urgent staff calls, changes to higher care levels, cardiac arrests or death, in 96 h following randomisation. Secondary outcomes were the proportion of patients requiring acute treatment changes, and the 30-day and hospital mortality. In the 96 h following randomisation, 113 (56%) patients in the monitored arm and 116 (58%) in the control arm (OR 0.94, 95% CI 0.63-1.40, p = 0.76) had a major event. An acute change in treatment was necessary in 107 (53%) monitored patients and 101 (50%) control patients (OR 0.55, 95% CI 0.87-1.29). Thirty-four (17%) monitored patients and 35 (17%) control patients died within 30 days. Thirteen patients in the control group received full five-channel monitoring at the request of the ward staff. We conclude that mandated electronic vital signs monitoring in high risk medical and surgical patients has no effect on adverse events or mortality.

System and method for communicating environmentally-based medical advice

Abstract: A method and system for delivering advice to patients suffering from respiratory conditions based on changes in environment, such as weather or air quality changes. This system includes a patient specific model which uses input environmental data to predict changes in the patient's condition. The model is developed from an analysis of patient's responses to a variety of environmental triggers, and can be refined with time. The model can include only those specific triggers appropriate to a patient, can include the delay between the change in environment and change in condition of the patient, and can be run with data which is geographically localized to the patient's location. Conveniently the models can be run on personal devices held by the patients, such as mobile telephones, which are in communication with a server and/or a provider of environmental data.

Combined Transfer Function Analysis and Modelling of Cerebral Autoregulation

Abstract: The clinical importance of cerebral autoregulation has resulted in a significant body of literature that attempts both to model the underlying physiological processes and to estimate the mathematical relationships between clinically measurable variables, the most common of which are Arterial Blood Pressure and Cerebral Blood Flow Velocity. These approaches have, however, rarely been used together to interpret clinical data. A simple model of cerebral autoregulation is thus proposed here, based on a flow dependent feedback mechanism with gain and time constant that adjusts arterial compliance. Analysis of this model shows that it closely approximates a second order system for typical values of physiological parameters. The model parameters can be optimally estimated from available experimental data for the Impulse Response (IR), yielding physiologically reasonable values, although there is one free parameter that must be fixed. The effects of changes in feedback gain and time constant are found to be significant on the predicted IR and can thus be estimated robustly from experimental data. The effects of elevated baseline Intracranial Pressure (ICP) are found to be exactly equivalent to a reduced feedback gain, although the solution is much less sensitive to the former effect. A transfer function approach can be used to estimate autoregulation status clinically using a physiologically-based model, thus providing greater insight into the processes that govern cerebral autoregulation.

Application of an intuitive novelty metric for jet engine condition monitoring

Abstract: Application of novelty detection to a new class of jet engine is considered within this paper, providing a worked example of the steps necessary for constructing a model of normality. Abnormal jet engine vibration signatures are automatically identified with respect to a training set of normal examples. Pre-processing steps suitable for this area of application are investigated. An intuitive metric for assigning novelty scores to patterns is introduced, with benefits for reducing model sensitivity to noise, and in pruning patterns from the model training set.

Learning shape for jet engine novelty detection

Abstract: Application of a neural network approach to data exploration and the generation of a model of system normality is described for use in novelty detection of vibration characteristics of a modern jet engine. The analysis of the shape of engine vibration signatures is shown to improve upon existing methods of engine vibration testing, in which engine vibrations are conventionally compared with a fixed vibration threshold. A refinement of the concept of “novelty scoring” in this approach is also presented.

Learning shape for jet engine novelty detection

Abstract: Application of a neural network approach to data exploration and the generation of a model of system normality is described for use in novelty detection of vibration characteristics of a modern jet engine. The analysis of the shape of engine vibration signatures is shown to improve upon existing methods of engine vibration testing, in which engine vibrations are conventionally compared with a fixed vibration threshold. A refinement of the concept of “novelty scoring” in this approach is also presented.

Detection of apnoeic and breathing activity through pole-zero analysis of the SpO/sub 2/ signal

Abstract: A new method using autoregressive modelling and pole tracking is proposed to detect cyclical activity within the oxygen saturation signal, SpO2, for subjects with Obstructive Sleep Apnoea (OSA). OSA is a sleep condition whereby the up- per airway is obstructed and a cessation in respiration (apnoea) occurs. The three types of detected activity include: apnoea, mix, and normal breathing ,w here 'mix' refers to breathing with a low-frequency component. Overall classifications produced by the analysis are in close agreement with expert scoring of the database. Furthermore, the pole-zero analysis method allows, for the first time, the mix sections to be identified automatically.

On the Weighted-Average Relationship Between Plasma Glucose and HbA1c

Abstract: HbA1c (A1C) is widely used to assess glycemic control in clinical and research settings, but the precise relationship between A1C and preceding self-monitored plasma glucose measurements is recognized to be complex. It has been reported that measuring plasma glucose levels in the 120 days before an A1C measurement has a nonuniform effect on the result depending on the time that has elapsed between the glucose level and subsequent A1C measurement (1). Tahara and Shima (2) attempted to model this weighted-average …