Biomedical data analysis group

The Biomedical data analysis group at the University of Stavanger has its basis in a number of active researchers with a proven track record. The research area's scope is data analysis encompassing signal and image processing statistical analysis applied to a wide variety of projects within biology and medicine. The group is involved in multidisciplinary research with both national and international collaborators.

Group members

Research profile

The research group is cross disciplinary as it relies increasingly on ideas and approaches taken from a number of scientific disciplines and application areas such as signal and image analysis, statistics, pattern recognintion, biology, chemistry and medicine. The main reseach activites are based on a solid foundation of competencies within these fields as documented by an extensive publications history, held by the scientists associated with the program. The group is also able to develop new methodology were existing methods do not exist.

Collaboration

Since 1996 the work within various application fields of signal analysis has been conducted in colloboration with both Norwegian and international research groups, some of which are listed below along with selected publications:

Research Areas

Our biomedically applied projects involves projects within emergency medicine, cardiology and biology, applying both signal and image analysis methods.

Management and analysis of resuscitation data

In Norway alone resuscitation is started on approximately 3000 patients with cardiac arrest out-of-hospital per year plus a considerable number in-hospital. Our activities directed towards therapy of acute cardiac arrest has been going on since 1996 and our previous research results within this field have been extensively published in highly reputed international medical and signal processing journals. Eftestøl contributed as coauthor on a chapter on \emph{Analysis and Predictive Value of the Ventricular Fibrillation Waveform} in the second edition of the book: \emph{Cardiac Arrest - The Science and Practice of Resuscitation Medicine} published by Cambridge University Press in 2007. We collaborate with several medical groups, both on national and international level. This project has been going on since the end of 1996 in collaboration with Laerdal Medical, The Cardiopulmonary Resuscitation Research Group in Oslo, The Circulation Research Group in Trondheim, Universitatsklinik fur Notfallsmedizin in Vienna, The Department of Anaestesiology Critical Care and Emergency Medicine in Innsbruck and The Signal Processing Research Group in Bilbao.

Analysis of magnetic resonance images and intercardial ECG from patients with myocardial infarction

In many patients having suffered from myocardial infarction the risk of suffering cardiac arrest will be considered severe enough for implantation of an internal cardiac defibrillater (ICD). The ICD monitors the cardiac rhythm represented by an electrogram (EGM). The ICD analyses the cardiac rhythm continuously by analysing the EGM. An electric countershock is provided if cardiac arrest is detected. It is interesing to study if changes can be detected in the electrical conductive system in the heart. Such changes can occur if the dead scar tissue in the infarcted heart changes. Therefore we study changes in the EGM over time in ICD patients as well as analysing magnetic resonance images of the myocardial scar tissue to see if there are differences in those suffering from cardiac arrest to those who do not. If methods can be found to predict which patients will suffer from cardiac arrest, unneccessary implantations can be avoided. This project has been going on since the end of 2006 in collaboration with Dr Leik Woie, Dr Stein ørn at the department of cardiology at the Stavanger University Hospital (SUS).\cite{woie08,woie08a,woie08b}.

Eukaryotic Gene Prediction

With the use of effective sequencing methods genomic information of organisms is growing exponentially with time. One of the important and nontrivial tasks is to develop algorithms that effectively can identify genes. Genes are clustered areas within the DNA double helix that code for proteins. In eukaryotic DNA, genes generally consist of coding regions (called exons) and noncoding regions (introns). The set of all nucleotides between the start of the first exon and the end of the last exon in a gene is called the open reading frame (ORF), og simply the frame. Proteins are translated from a copy of the gene (mRNA) where introns have been removed from the copy (pre-mRNA) and exons are joined together, a process called splicing. In alternative splicing, exon regions of a gene can be joined in different ways leading to different mRNA molecules and different proteins. It is therefore of importance to identify reliably the start of a gene, its exons and introns (if present) as well as the end of the gene. We are following several directions for predicting genes, both analysing coding and non-coding areas by content and by analysing the transition areas specifically. This project has been going on in collaboration with G. Schuster and students from Hochschule für Technik Rapperswil, Swiss since 2006 with UiS participation from Tom Ryen, Sven Ole Aase, Peter Ruoff, Thomas Kjosmoen and Trygve Eftestøl.\cite{eftes06}

Competencies

The common goal of our group is to be able to identify and handle problems within biology and medicine demanding skills in data analysis. Recently we have seen a growing awareness of the possibilities offered by data analysis by the biologists and clinicians. As data storage capabilities increases, the size of biological and biomedical databases increase increase in the hsopitals. This offers possibilities for research. To do this research efficiently, some key competencies are essential: