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Abstract:

This research focuses on the development of algorithms to extract diagnostic information from the ECG signal, which can be used to improve automatic detection systems and home monitoring solutions. In the first part of this work, a generically applicable algorithm for model selection in kernel principal component analysis is presented, which was inspired by the derivation of respiratory information from the ECG signal. This method not only solves a problem in biomedical signal processing, but more importantly offers a solution to a long-standing problem in the field of machine learning. Next, a methodology to quantify the level of contamination in a segment of ECG is proposed. This level is used to detect artifacts, and to improve the performance of different classifiers, by removing these artifacts from the training set. Furthermore, an evaluation of three different methodologies to compute the ECG-derived respiratory signal is performed. It is shown that for long-term signals with transients and non-stationarities, the R-peak amplitude offers the best solution taking into account computational complexity, and resemblance between the estimated and real respiratory signals. Thenbsp;step of this work covers the quantification of the cardiorespiratory interactions by means of phase rectified signal averaging, information dynamics and subspace projections. These methodologies provide complementary information to the typical heart rate variability analysis, for the assessment of the autonomic control. All these algorithms are applied in two main fields: sleep and epilepsy. In particular, the effect of sleep apnea on the ECG and respiration is analyzed, and based on this effect two new features are proposed and used to discriminate between normal activity and apnea episodes. These two features quantify the changes in the morphology of the ECG and assess the cardiorespiratory interactions during apnea events. A similar set of features is extracted and investigated for use in epileptic seizure detection. The ECG and the ECG-derived respiratory (EDR) signals are used to improve early detection of seizures and are promising for the development of accurate and robust closed-loop systems in epilepsy. In this respect, features based on the morphology of the ECG, assessed by means of principal component analysis are proposed. In addition, in collaboration with pediatric neurologists Prof. Dr. K. Jansen and L. Lagae, ways to early detect seizures in childhood epilepsy were investigated, achieving accuracies of 93% and 85% for the detection of partial and generalized seizures, respectively. Features extracted from the ECG, such as those used in heart rate variability (HRV) analysis, together with the analysis of cardiorespiratory interactions reveal important autonomic dysfunctions in children suffering from West syndrome and absence epilepsy. These more fundamental findings may play an important rolenbsp; understanding epilepsy, and in a long-term can change clinical practice and the associated treatment procedures.