Classification of paroxysmal and persistent atrial fibrillation in ambulatory ECG recordings
Författare
Summary, in English
The problem of classifying short atrial fibrillatory segments in ambulatory ECG recordings as being either paroxysmal or persistent is addressed by investigating a robust approach to signal characterization. The method comprises preprocessing, estimation of the dominant atrial frequency for the purpose of controlling the subbands of a filter bank, and computation of the relative subband (harmonics) energy and the subband sample entropy. Using minimum-error-rate classification of different feature vectors, a dataset consisting of 24-h ambulatory recordings from 50 subjects with either paroxysmal (26) or persistent (24) atrial fibrillation (AF) was analyzed on a 10-s segment basis; a total of 212196 segments were classified. The best performance in terms of area under the receiver operating characteristic curve was obtained for a feature vector defined by the subband sample entropy of the dominant atrial frequency and the relative harmonics energy, resulting in a value of 0.923, whereas that of the dominant atrial frequency was equal to 0.826. It is concluded that paroxysmal and persistent AF can be discriminated from short segments with good accuracy at any time of an ambulatory recording.
Publiceringsår
2011
Språk
Engelska
Sidor
1441-1449
Publikation/Tidskrift/Serie
IEEE Transactions on Biomedical Engineering
Volym
58
Dokumenttyp
Artikel i tidskrift
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Medical Engineering
Nyckelord
- Atrial fibrillation
- atrial organization
- dominant atrial frequency
- electrocardiogram
- filter bank
- hidden Markov model
- sample entropy
Status
Published
Forskningsgrupp
- Signal Processing
ISBN/ISSN/Övrigt
- ISSN: 1558-2531