Frequency tracking of atrial fibrillation using hidden Markov models.
Author
Summary, in English
A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.
Publishing year
2008
Language
English
Pages
502-511
Publication/Series
IEEE Transactions on Biomedical Engineering
Volume
55
Issue
2
Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Medical Engineering
Status
Published
Research group
- Signal Processing
ISBN/ISSN/Other
- ISSN: 1558-2531