Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The accumulation and relatively rapid removal of fluid in haemodialysis patients is often accompanied by intradialytic hypotension (IDH). Current patient monitoring during haemodialysis includes intermittent measurements of tympanic temperature, blood pressure and haematocrit. However, this information is mostly used retrospectively rather than as a means for preventing adverse events. We suggest the use of a probabilistic data fusion model based on dialysis vital sign data to predict IDH. We continuously monitored the vital signs of 40 haemodialysis patients during 8 sessions over a 6-month period in the Oxford Renal Unit. The study involved non-invasively monitoring the heart rate, blood oxygen saturation, systolic and diastolic blood pressures as well as the tympanic temperature throughout each dialysis session. The 4-dimensional vital sign data was initially visualised on 2D projections using the Neuroscale algorithm. The projections show a distinction between data from unstable and stable patients, with data from hypotensive events appearing outside the region of the 2D projection corresponding to "normal" physiology. A data fusion model based on an estimate of the probability density function of data from stable patients was then created. With this model, instabilities in patient physiology can be identified, and the adverse event can be predicted ahead of time in some cases.


Journal article


Computing in Cardiology

Publication Date





967 - 970