Key result
A new electrocardiogram delineation method using a hidden Markov tree model and wavelet coefficients achieved good results in P-wave delineation on a database for atrial fibrillation risk analysis.
Why the study?
Does a hidden Markov tree model improve ECG delineation, particularly for P-waves, in a database for atrial fibrillation risk analysis?
Does a hidden Markov tree model improve ECG delineation, particularly for P-waves, in a database for atrial fibrillation risk analysis?
A novel hidden Markov tree model using wavelet coefficients demonstrates good performance for ECG delineation, particularly for P-waves in atrial fibrillation risk analysis.
May support automated P-wave analysis in AF databases; leaves open prospective clinical validation before practice change.
A new electrocardiogram (ECG) delineation method is proposed, which uses a hidden Markov tree model. The aim of this approach is, on the one hand, to use wavelet coefficients to characterize the different ECG waves, and, on the other hand, to link these coefficients by a tree structure enabling wave change to be detected. By associating this method with a fusion method between scales based on the concept of context, good results are obtained on a special database created for the risk analysis of atrial fibrillation, particularly in P-wave delineation.
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Graja et al. (2005) studied Atrial fibrillation. Hidden Markov tree model for ECG delineation was evaluated on ECG wave delineation (particularly P-wave). A new electrocardiogram delineation method using a hidden Markov tree model and wavelet coefficients achieved good results in P-wave delineation on a database for atrial fibrillation risk analysis.
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