This thesis focuses on signal analysis of electrocardiograms (ECG) from out-of-hospital cardiac arrested patients. The application of such methods may eventually contribute in guiding therapy towards improved survival rates which in general are dismally low, but varies among different ambulance systems depending on time from arrest to first electrical defibrillation given to the patient. One of the possible reasons for this is that a large part of the valuable therapy time is wasted in futile attempts to restart the heart by electrical defibrillations. Using this time to provide precordial compressions and ventilations to establish and keep up an artificial supply of oxygenated blood would serve the patient better. It would improve the heart condition and thus increase the chance of successful defibrillation outcome. By predicting defibrillation outcome, the ratio of failured defibrillations can be decreased. The ability to monitor therapeutic efficacy can help the rescuer to optimise performance. A decision support system involving ECG signal analysis to extract descriptive features has been investigated for these purposes in earlier work. We propose to use a pattern recognition framework for the decision support system. In contrast to most earlier work with one-dimensional features, this allows analysis of multivariate information. In our experiments we demonstrate that performance both in outcome prediction and monitoring is better when the analysis is based on combined rather than one-dimensional features. We also propose and experimentally verify methods firstly to control performance and secondly to ensure that performance results are reliable. Another problem also has to be resolved. The precordial compressions and ventilations cause artefacts in the ECG so that treatment has to be stopped during analysis. We propose using adaptive filters for removing such artefacts. These filters use reference signals providing information correlated to components in the artefacts. In one of our experiments we mix human ECG with artefacts from animal ECG and show that the adaptive filter is successful in restoring the original human ECG signal.

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for adressing the problem. The focus of the dissertation is on signal representation and compression by the use of optimization theory, more shortest path methods. Several new signal compression algorithms are presented. They are based on the coding of line segments which are used to spproximate, and thereby represent, the signal. These segments are fit in a way that is optimal given some constraints on the solution. By formulating the compession problem as a graph theory problem, shortest path methods can be applied in order to yeild optimal compresson with respect to the given constraints. The approaches focused on in this dissertaion mainly have their origin in ECG comression and is often referred to as time domain compression methods. Coding by time domain methods is based on the idea of extracting a subset of significant signals samples to represent the signal. The key to a successful algoritm is a good rule for determining the most significant samples. Between any two succeeding samples in the extracted smaple set, different functions are applied in reconstruction of the signal. These functions are fitted in a wy that guaratees minimal reconstruction error under the gien constraints. Two main categories of compression schemes are developed: 1. Interpolating methods, in which it is insisted on equality between the original and reconstructed signal at the points of extraction. 2. Non-interpolating methods, where the inerpolatian restriction is released. Both first and second order polynomials are used in reconstruction of the signal. There is solso developed an approach were multiple error measures are applied within one compression algorithm. The approach of extracting the most significant smaples are further developed by measuring the samples in terms of the number of bits needed to encode such samples. This way we develop an approach which is optimal in the ratedistortion sense. Although the approaches developed are applicable to any type of signal, the focus of this dissertaion is on the compression of electrodiogram (ECG) signals and image contours, ECG signal compression has traditionally been

Diss. Karlskrona : Blekinge tekniska högskola, 2007.

Diss. Karlskrona : Blekinge tekniska högskola, 2007.

Licentiatavhandling Ronneby : Blekinge tekniska högskola, 2006.

Diss. (sammanfattning) Luleå : Luleå tekniska univ., 2004. / Härtill 4 uppsatser.

Licentiatavhandling Linköping : Linköpings universitet, 2004.

Lic.-avh. Ronneby : Blekinge tekniska högskola, 2006.

Diss. Karlskrona : Blekinge tekniska högskola, 2007.

Diss. Uppsala : Uppsala universitet, 2007.