Atrial Fibrillation Analysis Based on Blind Source Separation in 12-lead ECG

Authors: Pei-Chann Chang, Jui-Chien Hsieh, Jyun-Jie Lin, Feng-Ming Yeh

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330633

References:

[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.