Mattias Ohlsson

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Despite many attempts to improve the management of acute myocardial infarction, only small trends to shorter time intervals before treatment have been reported. The self-care solution developed by the European EPI-MEDICS project (2001-2004) is a novel, very affordable, easy-to-use, portable, and intelligent Personal ECG Monitor (PEM) for the early detection(More)
Unraveling functional and ancestral relationships between proteins as well as structure-prediction procedures require powerful protein-alignment methods. A structure-alignment method is presented where the problem is mapped onto a cost function containing both fuzzy (Potts) assignment variables and atomic coordinates. The cost function is minimized by using(More)
We use artificial neural networks (ANNs) to detect signs of acute myocardial infarction (AMI) in ECGs. The 12-lead ECG is decomposed into Hermite basis functions, and the resulting coefficients are used as inputs to the ANNs. Furthermore, we present a case-based method that qualitatively explains the operation of the ANNs, by showing regions of each ECG(More)
Estimation of the generalization performance for classification within the medical applications domain is always an important task. In this study we focus on artificial neural network ensembles as the machine learning technique. We present a numerical comparison between five common resampling techniques: k-fold cross validation (CV), holdout, using three(More)
Artificial neural networks can be used to recognize lead reversals in the 12-lead electrocardiogram at very high specificity, and the sensitivity is much higher than that of a conventional interpretation program. The neural networks developed in this and an earlier study for detection of lead reversals, in combination with an algorithm for the right(More)
BACKGROUND Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a(More)
A novel algorithm for particle tracking is presented and evaluated. It is based on deformable templates that converge using a deterministic annealing algorithm. These deformable templates are initialized by Hough transforms. The algorithm, which eeectively represents a merger between neuronic decision making and parameter tting, naturally lends itself to(More)
A mean field feedback artificial neural network algorithm is developed and explored for the set covering problem. A convenient encoding of the inequality constraints is achieved by means of a multilinear penalty function. An approximate energy minimum is obtained by iterating a set of mean field equations, in combination with annealing. The approach is(More)
UNLABELLED The purpose of this study was to develop a computer-based method for automatic detection and localization of coronary artery disease (CAD) in myocardial bull's-eye scintigrams. METHODS A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest-stress scintigraphy and coronary angiography within 3 mo was studied.(More)