Improving the transition modelling in hidden Markov models for ECG segmentation
نویسندگان
چکیده
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.
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