Frequency tracking of atrial fibrillation using hidden Markov models
(2006) 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS '06. In IEEE Press p.14061409 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:
http://lup.lub.lu.se/record/1216847
 author
 Sandberg, Frida ^{LU} ; Stridh, Martin ^{LU} and Sörnmo, Leif ^{LU}
 organization
 publishing date
 2006
 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
 1557170X
 ISBN
 1424400325
 DOI
 10.1109/IEMBS.2006.259677
 language
 English
 LU publication?
 yes
 id
 0affb23667f9428aafe14b9edf6c0394 (old id 1216847)
 date added to LUP
 20080820 15:01:16
 date last changed
 20161013 04:33:09
@misc{0affb23667f9428aafe14b9edf6c0394, 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 = {1424400325}, issn = {1557170X}, language = {eng}, pages = {14061409}, 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}, }