Advanced

Frequency tracking of atrial fibrillation using hidden Markov models

Sandberg, Frida LU ; Stridh, Martin LU and Sörnmo, Leif LU (2006) 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS '06. In IEEE Press p.1406-1409
Abstract
A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An... (More)
A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An evaluation is performed using simulated AF signals where noise obtained from ECG recordings have been added at different SNR. The results show that the use of HMM considerably reduces the average RMS error associated with the frequency tracking: at 5 dB SNR the RMS error drops from 1.2 Hz to 0.2 Hz. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IEEE Press
pages
1406 - 1409
conference name
28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS '06.
external identifiers
  • Scopus:34047121683
ISSN
1557-170X
ISBN
1-4244-0032-5
DOI
10.1109/IEMBS.2006.259677
language
English
LU publication?
yes
id
0affb236-67f9-428a-afe1-4b9edf6c0394 (old id 1216847)
date added to LUP
2008-08-20 15:01:16
date last changed
2016-10-13 04:33:09
@misc{0affb236-67f9-428a-afe1-4b9edf6c0394,
  abstract     = {A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An evaluation is performed using simulated AF signals where noise obtained from ECG recordings have been added at different SNR. The results show that the use of HMM considerably reduces the average RMS error associated with the frequency tracking: at 5 dB SNR the RMS error drops from 1.2 Hz to 0.2 Hz.},
  author       = {Sandberg, Frida and Stridh, Martin and Sörnmo, Leif},
  isbn         = {1-4244-0032-5},
  issn         = {1557-170X},
  language     = {eng},
  pages        = {1406--1409},
  series       = {IEEE Press},
  title        = {Frequency tracking of atrial fibrillation using hidden Markov models},
  url          = {http://dx.doi.org/10.1109/IEMBS.2006.259677},
  year         = {2006},
}