Abstract / truncated to 115 words (read the full 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 ... toggle 6 keywords

electrocardiogram (ecg) long-term ecg monitoring sleep monitoring epilepsy monitoring machine learning cardiorespiratory interactions


Varon, Carolina
KU Leuven
Publication Year
Upload Date
Sept. 30, 2017

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