Semi-supervised learning of probabilistic models for ECG segmentation

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn 26 I, Issue , 2004, Pages 434-437

  Hughes, Nicholas P ^a   Roberts, Stephen J ^a   Tarassenko, Lionel ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; ELECTROCARDIOGRAPHY; MARKOV PROCESSES; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY; WAVEFORM ANALYSIS; WAVELET TRANSFORMS;

DATA COLLECTION; PROBABILISTIC MODELS; SEMI-SUPERVISED LEARNING; VENTRICULAR REPOLARIZATION;

LEARNING SYSTEMS;

EID: 11044237365     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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2. A. Djohan, T. Q. Nguyen, and W. J. Tompkins, "ECG compression using discrete symmetric wavelet transform," in Proceedings IEEE-EMBS, Montreal, 1995, pp. 167-168.
3. D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis. Cambridge University Press, 2000.
4. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, 1989.
5. N. P. Hughes, L. Tarassenko, and S. J. Roberts, "Markov models for automated ECG interval analysis," in Proceedings NIPS 16, 2004.
6. S. E. Levinson, "Continuously variable duration hidden Markov models for automatic speech recognition," Computer Speech and Language, vol. 1, no. 1, pp. 29-15, 1986.
7. L. Thoraval, G. Carrault, and F. Mora, "Continuously variable duration HMMs for ECG segmentation," in Proceedings IEEE-EMBS, Paris, 1992, pp. 529-530.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society Series B, vol. 39, no. 1, pp. 1-38, 1977.
9. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford University Press, 1995.
10. Y. Bengio, "Markovian models for sequential data," Neural Computing Surveys, vol. 2, pp. 129-162, 1999.