Improving the transition modelling in hidden Markov models for ECG segmentation: Proceedings of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009)

The segmentation of ECG signal is a useful tool for the diagnosis of cardiac diseases. However, the state-of-the-art methods use hidden Markov models which do not adequately model the transitions between successive waves. This paper uses two methods which attempt to overcome this limitation: a HMM state scission scheme which prevents ingoing and outgoing transitions in the middle of the waves and a bayesian network where the transitions are emission-dependent. Experiments show that both methods improve the results on pathological ECG signals.