Lionel Tarassenko

TL;DR: A novel method for estimating respiratory rate from the ECG which fuses frequency information from the two methods, and does not underperform at the lower or higher respiratory rates.

TL;DR: A novel method for Support Vector Machines of including information from a second class for novelty detection is described and results from the application to Jet Engine vibration analysis are given.

TL;DR: A system to extract diagnostic information from jet engine carcass vibration data using neural network techniques, which provides good separation between usual and unusual vibration signatures but, given the small number of examples of normal engines, the resulting representation of normality may be overfitting the training data.

TL;DR: In this paper, a method of displaying medical data, particularly data representative of the condition of patients suffering from chronic medical conditions such as asthma, diabetes and hypertension, is presented, where the display consists of two graphical elements, one of which indicates the current value of a parameter indicative of the patient's condition, this being displayed against another graphical element which represents a model of normality for that patient.

TL;DR: A finite-state machine–based approach for modeling COPD exacerbation is proposed to gain a deeper insight into COPD patient condition during home monitoring to take account of the time course of symptoms and a robust algorithm based on short-period trend analysis and logistic regression using vital signs derived from a pulse oximeter is developed to predict exacerbations.