Title :
Semi-supervised learning of probabilistic models for ECG segmentation
Author :
Hughes, Nicholas P. ; Roberts, Stephen J. ; Tarassenko, Lionel
Author_Institution :
Dept. of Eng. Sci., Oxford Univ., UK
Abstract :
We present a novel semi-supervised learning algorithm, based upon the EM algorithm for maximum likelihood estimation, which can be used to learn probabilistic models from subjectively labelled data. We demonstrate the method on the task of automated ECG segmentation, with a particular emphasis on the accurate measurement of the QT interval. In addition we discuss the use of wavelet methods for the representation of the ECG, and advanced duration modelling techniques for hidden Markov models applied to ECG segmentation.
Keywords :
electrocardiography; hidden Markov models; learning (artificial intelligence); maximum likelihood estimation; medical signal processing; physiological models; ECG segmentation; EM algorithm; QT interval; advanced duration modelling; hidden Markov models; maximum likelihood estimation; probabilistic models; semi-supervised learning; wavelet methods; Biomedical measurements; Cardiology; Data engineering; Electrocardiography; Heart rate interval; Hidden Markov models; Maximum likelihood estimation; Rhythm; Semisupervised learning; Signal analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-8439-3
DOI :
10.1109/IEMBS.2004.1403187