Abstract
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.
Original language | English |
---|---|
Title of host publication | ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning |
Pages | 141-146 |
Number of pages | 6 |
Publication status | Published - 2009 |
Externally published | Yes |
Event | 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 - Bruges, Belgium Duration: 22 Apr 2009 → 24 Apr 2009 |
Conference
Conference | 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 |
---|---|
Country/Territory | Belgium |
City | Bruges |
Period | 22/04/09 → 24/04/09 |
Keywords
- ECG segmentation Transition modelling Hidden Markov models ICTEAM:MLAI