PubMed Health⌕ Search

Biomedical subjects

Lionel Tarassenko

Publications and source records attributed to Lionel Tarassenko.

10 recordsLinked to original sources
PubMed2005

Quantifying errors in spectral estimates of HRV due to beat replacement and resampling.

Spectral estimates of heart rate variability (HRV) often involve the use of techniques such as the fast Fourier transform (FFT), which require an evenly sampled time series. HRV is calculated from the variations in the beat-to-beat (RR) interval timing of the cardiac cycle which are inherently irregularly spaced in time. In order to produce an evenly sampled time series prior to FFT-based spectral estimation, linear or cubic spline resampling is usually employed. In this paper, by using a realistic artificial RR interval generator, interpolation and resampling is shown to result in consistent over-estimations of the power spectral density (PSD) compared with the theoretical solution. The Lomb-Scargle (LS) periodogram, a more appropriate spectral estimation technique for unevenly sampled time series that uses only the original data, is shown to provide a superior PSD estimate. Ectopy removal or replacement is shown to be essential regardless of the spectral estimation technique. Resampling and phantom beat replacement is shown to decrease the accuracy of PSD estimation, even at low levels of ectopy or artefact. A linear relationship between the frequency of ectopy/artefact and the error (mean and variance) of the PSD estimate is demonstrated. Comparisons of PSD estimation techniques performed on real RR interval data during minimally active segments (sleep) demonstrate that the LS periodogram provides a less noisy spectral estimate of HRV.

Adult↗
PubMed2005

A real-time, mobile phone-based telemedicine system to support young adults with type 1 diabetes.

Telemedicine systems have been proposed as a means of supporting people with diabetes in the self-management of their condition. Requirements for monitoring parameters of care, including glycaemic control, extent of analysis and interpretation of data, patient-clinician contacts, and involvement of a multidisciplinary care team with effective communication, can be addressed by telemedicine systems. We describe the development and implementation of an innovative real-time telemedicine system based around transmission and feedback of data to and from a mobile phone. Proprietary Java-based programs were used to link a blood glucose meter to a mobile phone. In addition to immediate transmission of blood glucose data, information about insulin dose, eating patterns and physical exercise were collected. Immediate feedback to the phone included a colour histogram to draw attention to levels of control over glycaemia over the previous two weeks. Clinicians supporting patients had access to summary screens identifying users not testing, and those with levels of blood glucose outside pre-defined limits. More detailed graphical displays of data were used to provide data about control of insulin dose and the degree to which it was modified in response to diet and exercise. The system has been evaluated in a clinical trial conducted in secondary care and is now being adapted for use in a trial in primary care, which is designed to assess its effectiveness in providing integrated management for the patient, general practitioner and pharmacist.

Adolescent↗
PubMed2005

A randomized controlled trial of the effect of real-time telemedicine support on glycemic control in young adults with type 1 diabetes (ISRCTN 46889446).

OBJECTIVE: To determine whether a system of telemedicine support can improve glycemic control in type 1 diabetes. RESEARCH DESIGN AND METHODS: A 9-month randomized trial compared glucose self-monitoring real-time result transmission and feedback of results for the previous 24 h in the control group with real-time graphical phone-based feedback for the previous 2 weeks together with nurse-initiated support using a web-based graphical analysis of glucose self-monitoring results in the intervention group. All patients aged 18-30 years with HbA(1c) (A1C) levels of 8-11% were eligible for inclusion. RESULTS: A total of 93 patients (55 men) with mean diabetes duration (means +/- SD) 12.1 +/- 6.7 years were recruited from a young adult clinic. In total, the intervention and control groups transmitted 29,765 and 21,400 results, respectively. The corresponding median blood glucose levels were 8.9 mmol/l (interquartile range 5.4-13.5) and 10.3 mmol/l (6.5-14.4) (P < 0.0001). There was a reduction in A1C in the intervention group after 9 months from 9.2 +/- 1.1 to 8.6 +/- 1.4% (difference 0.6% [95% CI 0.3-1.0]) and a reduction in A1C in the control group from 9.3 +/- 1.5 to 8.9 +/- 1.4% (difference 0.4% [0.03-0.7]). This difference in change in A1C between groups was not statistically significant (0.2% [-0.2 to 0.7, P = 0.3). CONCLUSIONS: Real-time telemedicine transmission and feedback of information about blood glucose results with nurse support is feasible and acceptable to patients, but to significantly improve glycemic control, access to real-time decision support for medication dosing and changes in diet and exercise may be required.

Adult↗
PubMed2004

Non-linear survival analysis using neural networks.

We describe models for survival analysis which are based on a multi-layer perceptron, a type of neural network. These relax the assumptions of the traditional regression models, while including them as particular cases. They allow non-linear predictors to be fitted implicitly and the effect of the covariates to vary over time. The flexibility is included in the model only when it is beneficial, as judged by cross-validation. Such models can be used to guide a search for extra regressors, by comparing their predictive accuracy with that of linear models. Most also allow the estimation of the hazard function, of which a great variety can be modelled. In this paper we describe seven different neural network survival models and illustrate their use by comparing their performance in predicting the time to relapse for breast cancer patients.

Breast Neoplasms↗
PubMed2003

Neural connections that compute.

The UK's Foresight Cognitive Systems Project brings together researchers in the life sciences and physical sciences to see where they can learn from one another and to debate, and plan, the future of research in cognitive systems. The project, a part of the UK government's Foresight initiative, sets out to identify potential opportunities for the economy or society from new science and technology. Through a series of research reviews, the project has created 'snap shots' of research in cognitive systems. A major conference in Bristol in September will consider series of research manifestos created by interdisciplinary groups.

Cognition↗
PubMed2003

A dynamical model for generating synthetic electrocardiogram signals.

A dynamical model based on three coupled ordinary differential equations is introduced which is capable of generating realistic synthetic electrocardiogram (ECG) signals. The operator can specify the mean and standard deviation of the heart rate, the morphology of the PQRST cycle, and the power spectrum of the RR tachogram. In particular, both respiratory sinus arrhythmia at the high frequencies (HFs) and Mayer waves at the low frequencies (LFs) together with the LF/HF ratio are incorporated in the model. Much of the beat-to-beat variation in morphology and timing of the human ECG, including QT dispersion and R-peak amplitude modulation are shown to result. This model may be employed to assess biomedical signal processing techniques which are used to compute clinical statistics from the ECG.

Computer Simulation↗
PubMed2003

Comparison of predictability of epileptic seizures by a linear and a nonlinear method.

The performance of traditional linear (variance based) methods for the identification and prediction of epileptic seizures are contrasted with "modern" methods from nonlinear time series analysis. We note several flaws of design in demonstrations claiming to establish the efficacy of nonlinear techniques; in particular, we examine published evidence for precursor identification. We perform null hypothesis tests using relevant surrogate data to demonstrate that decreases in the correlation density prior to and during seizure may simply reflect increases in the variance.

Algorithms↗