Atrial Fibrillation Analysis Based on Blind Source Separation in 12-lead ECG
Atrial Fibrillation is the most common sustained
arrhythmia encountered by clinicians. Because of the invisible
waveform of atrial fibrillation in atrial activation for human, it is
necessary to develop an automatic diagnosis system. 12-Lead ECG
now is available in hospital and is appropriate for using Independent
Component Analysis to estimate the AA period. In this research, we
also adopt a second-order blind identification approach to transform
the sources extracted by ICA to more precise signal and then we use
frequency domain algorithm to do the classification. In experiment,
we gather a significant result of clinical data.
[1] Y. J. Lin, Y. B. Liu and C. C. Chu, "Incremental changes in QRS
duration predict mortality in patients with atrial fibrillation," PACE -
Pacing and Clinical Electrophysiology, vol. 32, pp. 1388-1394, 2009.
[2] S. A. Hunt, et al., "ACC/AHA guidelines for the evaluation and
management of chronic heart failure in the adult: Executive summary. A
report of the American College of Cardiology/American Heart
Association Task Force on Practice Guidelines (Committee to Revise the
1995 Guidelines for the Evaluation and Management of Heart Failure),"
Journal of the American College of Cardiology, vol. 38, pp. 2101-2113,
2001.
[3] R. Alcaraz and J. J. Rieta, "A review on sample entropy applications for
the non-invasive analysis of atrial fibrillation electrocardiograms,"
Biomedical Signal Processing and Control, 2009.
[4] V. Fuster, et al., "ACC/AHA/ESC 2006 guidelines for the management
of patients with atrial fibrillation: A report of the American College of
Cardiology/American Heart Association Task Force on practice
guidelines and the European Society of Cardiology Committee for
practice guidelines (Writing committee to revise the 2001 guidelines for
the management of patients with atrial fibrillation)," Circulation, vol.
114, pp. e257-e354, 2006.
[5] M. Stridh and L. Sörnmo, "Spatiotemporal QRST cancellation
techniques for analysis of atrial fibrillation," IEEE Transactions on
Biomedical Engineering, vol. 48, pp. 105-111, 2001.
[6] N. V. Thakor and Y. S. Zhu, "Applications of adaptive filtering to ECG
analysis: Noise cancellation and arrhythmia detection," IEEE
Transactions on Biomedical Engineering, vol. 38, pp. 785-794, 1991.
[7] F. Castells, J. J. Rieta, J. Millet and V. Zarzoso, "Spatiotemporal blind
source separation approach to atrial activity estimation in atrial
tachyarrhythmias," IEEE Transactions on Biomedical Engineering, vol.
52, pp. 258-267, 2005.
[8] Z. Huang, Y. Chen and M. Pan, "Time-frequency characterization of
atrial fibrillation from surface ECG based on Hilbert-Huang transform,"
Journal of Medical Engineering and Technology, vol. 31, pp. 381-389,
2007.
[9] P. S. Addison, J. N. Watson, G. R. Clegg, P. A. Steen and C. E.
Robertson, "Finding coordinated atrial activity during ventricular
fibrillation using wavelet decomposition," IEEE Engineering in
Medicine and Biology Magazine, vol. 21, pp. 58-61+65, 2002.
[10] P. Langley, J. P. Bourke and A. Murray, "Frequency analysis of atrial
fibrillation," in Computers in Cardiology, 2000, pp. 65-68.
[11] T. B. Garcia and N. E. Holtz, Introduction to 12-Lead ECG: The Art of
Interpretation: Jones and Bartlett Publishers, 2001.
[12] J. J. Rieta, V. Zarzoso, J. Millet-Roig, R. García-Civera and R.
Ruiz-Granell, "Atrial activity extraction based on blind source separation
as an alternative to QRST cancellation for atrial fibrillation analysis,"
2000, pp. 69-72.
[13] P. Comon, "Independent component analysis, A new concept?," Signal
Processing, vol. 36, pp. 287-314, 1994.
[14] A. Hyvärinen and E. Oja, "Independent component analysis: Algorithms
and applications," Neural Networks, vol. 13, pp. 411-430, 2000.
[15] A. Belouchrani, K. Abed-Meraim, J. F. Cardoso and E. Moulines, "A
blind source separation technique using second-order statistics," IEEE
Transactions on Signal Processing, vol. 45, pp. 434-444, 1997.
[1] Y. J. Lin, Y. B. Liu and C. C. Chu, "Incremental changes in QRS
duration predict mortality in patients with atrial fibrillation," PACE -
Pacing and Clinical Electrophysiology, vol. 32, pp. 1388-1394, 2009.
[2] S. A. Hunt, et al., "ACC/AHA guidelines for the evaluation and
management of chronic heart failure in the adult: Executive summary. A
report of the American College of Cardiology/American Heart
Association Task Force on Practice Guidelines (Committee to Revise the
1995 Guidelines for the Evaluation and Management of Heart Failure),"
Journal of the American College of Cardiology, vol. 38, pp. 2101-2113,
2001.
[3] R. Alcaraz and J. J. Rieta, "A review on sample entropy applications for
the non-invasive analysis of atrial fibrillation electrocardiograms,"
Biomedical Signal Processing and Control, 2009.
[4] V. Fuster, et al., "ACC/AHA/ESC 2006 guidelines for the management
of patients with atrial fibrillation: A report of the American College of
Cardiology/American Heart Association Task Force on practice
guidelines and the European Society of Cardiology Committee for
practice guidelines (Writing committee to revise the 2001 guidelines for
the management of patients with atrial fibrillation)," Circulation, vol.
114, pp. e257-e354, 2006.
[5] M. Stridh and L. Sörnmo, "Spatiotemporal QRST cancellation
techniques for analysis of atrial fibrillation," IEEE Transactions on
Biomedical Engineering, vol. 48, pp. 105-111, 2001.
[6] N. V. Thakor and Y. S. Zhu, "Applications of adaptive filtering to ECG
analysis: Noise cancellation and arrhythmia detection," IEEE
Transactions on Biomedical Engineering, vol. 38, pp. 785-794, 1991.
[7] F. Castells, J. J. Rieta, J. Millet and V. Zarzoso, "Spatiotemporal blind
source separation approach to atrial activity estimation in atrial
tachyarrhythmias," IEEE Transactions on Biomedical Engineering, vol.
52, pp. 258-267, 2005.
[8] Z. Huang, Y. Chen and M. Pan, "Time-frequency characterization of
atrial fibrillation from surface ECG based on Hilbert-Huang transform,"
Journal of Medical Engineering and Technology, vol. 31, pp. 381-389,
2007.
[9] P. S. Addison, J. N. Watson, G. R. Clegg, P. A. Steen and C. E.
Robertson, "Finding coordinated atrial activity during ventricular
fibrillation using wavelet decomposition," IEEE Engineering in
Medicine and Biology Magazine, vol. 21, pp. 58-61+65, 2002.
[10] P. Langley, J. P. Bourke and A. Murray, "Frequency analysis of atrial
fibrillation," in Computers in Cardiology, 2000, pp. 65-68.
[11] T. B. Garcia and N. E. Holtz, Introduction to 12-Lead ECG: The Art of
Interpretation: Jones and Bartlett Publishers, 2001.
[12] J. J. Rieta, V. Zarzoso, J. Millet-Roig, R. García-Civera and R.
Ruiz-Granell, "Atrial activity extraction based on blind source separation
as an alternative to QRST cancellation for atrial fibrillation analysis,"
2000, pp. 69-72.
[13] P. Comon, "Independent component analysis, A new concept?," Signal
Processing, vol. 36, pp. 287-314, 1994.
[14] A. Hyvärinen and E. Oja, "Independent component analysis: Algorithms
and applications," Neural Networks, vol. 13, pp. 411-430, 2000.
[15] A. Belouchrani, K. Abed-Meraim, J. F. Cardoso and E. Moulines, "A
blind source separation technique using second-order statistics," IEEE
Transactions on Signal Processing, vol. 45, pp. 434-444, 1997.
@article{"International Journal of Medical, Medicine and Health Sciences:52724", author = "Pei-Chann Chang and Jui-Chien Hsieh and Jyun-Jie Lin and Feng-Ming Yeh", title = "Atrial Fibrillation Analysis Based on Blind Source Separation in 12-lead ECG", abstract = "Atrial Fibrillation is the most common sustained
arrhythmia encountered by clinicians. Because of the invisible
waveform of atrial fibrillation in atrial activation for human, it is
necessary to develop an automatic diagnosis system. 12-Lead ECG
now is available in hospital and is appropriate for using Independent
Component Analysis to estimate the AA period. In this research, we
also adopt a second-order blind identification approach to transform
the sources extracted by ICA to more precise signal and then we use
frequency domain algorithm to do the classification. In experiment,
we gather a significant result of clinical data.", keywords = "12-Lead ECG, Atrial Fibrillation, Blind SourceSeparation, Kurtosis", volume = "4", number = "5", pages = "181-4", }
