Queue-based modelling and detection of parameters involved in stroke outcome.

We designed a queue-based model, and investigated which parameters are of importance when predicting stroke outcome. Medical record forms have been collected for 57 ischemic stroke patients, including medical history and vital sign measurement along with neurological scores for the first twenty-four hours of admission. The importance of each parameter is identified using multiple regression combined with a circular queue to iteratively fit outcome. Out of 39 parameters, the model isolated 14 which combined could estimate outcome with a root mean square error of 1.69 on the Scandinavian Stroke Scale, where outcome for patients were 36.75 ± 10.99. The queue-based model integrating multiple linear regression shows promising results for automatic selection of significant medically relevant parameters.

Simplifying EHR Overview of Critically Ill Patients Through Vital Signs Monitoring.

This paper presents a novel data-driven approach to graphical presentation of text-based electronic health records (EHR) while maintaining all textual information. We have developed the Patient Condition Timeline (PCT) tool, which creates a timeline representation of a patients' physiological condition during admission. PCT is based on electronical monitoring of vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on existing EHR to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the EHR critical events have taken place.

Visualizing patient journals by combining vital signs monitoring and natural language processing.

This paper presents a data-driven approach to graphically presenting text-based patient journals while still maintaining all textual information. The system first creates a timeline representation of a patients' physiological condition during an admission, which is assessed by electronically monitoring vital signs and then combining these into Early Warning Scores (EWS). Hereafter, techniques from Natural Language Processing (NLP) are applied on the existing patient journal to extract all entries. Finally, the two methods are combined into an interactive timeline featuring the ability to see drastic changes in the patients' health, and thereby enabling staff to see where in the journal critical events have taken place.

Video surveillance of epilepsy patients using color image processing.

This paper introduces a method for tracking patients under video surveillance based on a color marker system. The patients are not restricted in their movements, which requires a tracking system that can overcome non-ideal scenes e.g. occlusions, very fast movements, lighting issues and other moving objects. The suggested marker system consists of twelve unique markers that are located at each joint. By using a color marker system, each marker (if visible) can be found in every frame disregarding the possibility that it was occluded in the previous frame, compared to other tracking systems.

DTU BCI speller: an SSVEP-based spelling system with dictionary support.

In this paper, a new brain computer interface (BCI) speller, named DTU BCI speller, is introduced. It is based on the steady-state visual evoked potential (SSVEP) and features dictionary support. The system focuses on simplicity and user friendliness by using a single electrode for the signal acquisition and displays stimuli on a liquid crystal display (LCD). Nine healthy subjects participated in writing full sentences after a five minutes introduction to the system, and obtained an information transfer rate (ITR) of 21.94 ± 15.63 bits/min. The average amount of characters written per minute (CPM) is 4.90 ± 3.84 with a best case of 8.74 CPM. All subjects reported systematically on different user friendliness measures, and the overall results indicated the potentials of the DTU BCI Speller system. For subjects with high classification accuracies, the introduced dictionary approach greatly reduced the time it took to write full sentences.