Abstract
Clinical monitoring and pharmaceutical phaseone studies require feature extraction from the ECG signal in order to evaluate the state of a patient’s heart. Automatic annotation of the characteristic ECG waveforms (or so-called delineation) is therefore of great interest. Hidden Markov Models (HMM) coupled to wavelet transforms (WT) of the ECG signal offer significant improvements over standard heuristic delineation methods. Nevertheless, the choice of the WT parameters remains empirical rather than data-driven. In these conditions, suboptimal parameters for the WT may degrade the results very much. In this paper, an algorithm for the optimal selection of the WT parameter values is introduced. The model complexity is strongly reduced and the algorithm can adapt itself to the specificities of each ECG signal while avoiding redundancy, noise and useless information. Evaluation on recordings from the public MITQT database leads to results higher than with state of the art methods.
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References
Laakso M, Aberg A, Savola J, Pentikäinen PJ and Pyörälä K (1987) Diseases and drugs causing prolongation of the QT interval. American journal of Cardiology, 59(8), pp 862–5
Clifford G. D., Azuaje F. and McSharry P. E. (2006) Advanced Methods and Tools for ECG Data Analysis. Artech House Publishing, Boston/London.
Malik M. (2004) Errors and misconceptions in ECG measurement used for the detection of drug induced QT interval prolongation. Journal of Electrocardiology, 37, pp 25–33
Coast D.A., Stern R.M., Cano G.G. and Briller S.A. (1990) An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Transactions on Biomedical Engineering, 37(9), pp 826–836
Koski A. (1996) Modeling ECG signals with hidden Markov models. Artificial Intelligence in Medicine, 8(5), pp 453–471
Rabiner L.R. (1989) A Tutorial on Hidden Markov Models and Selected Applications In Speech Recognition. Proceedings of the IEEE, 77(2), pp 257–286
Hughes N., Tarassenko L. and Roberts S. (2004) Markov Models for Automated ECG Interval Analysis, Advances in Neural Information Processing Systems (NIPS), 16
Singh B. N., Tiwari A. K. (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing, 16(3), pp 175–287
Addison P.D. (2005) Wavelet Transform and the ECG: A Review. Physiological Measurements, 25, 155–199
Mallat S. (1999) A Wavelet Tour Of Signal Processing (Wavelet Analysis And Its Applications). IEEE Press, San Diego
Forney, G. D. (1973) The Viterbi Algorithm. Proc. of the IEEE, 61, pp 268–278.
Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C.-K. and Stanley H. E. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), pp 215
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de Lannoy, G., Frenay, B., Verleysen, M., Delbeke, J. (2009). Supervised ECG Delineation Using the Wavelet Transform and Hidden Markov Models. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_7
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DOI: https://doi.org/10.1007/978-3-540-89208-3_7
Publisher Name: Springer, Berlin, Heidelberg
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