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Моделирование и анализ данных

Издатель: Московский государственный психолого-педагогический университет

ISSN (печатная версия): 2219-3758

ISSN (online): 2311-9454

DOI: https://doi.org/10.17759/mda

Лицензия: CC BY-NC 4.0

Издается с 2011 года

Периодичность: 4 номера в год

Язык журнала: русский

Доступ к электронным архивам: открытый

 

Novel probabilistic algorithms for dynamic monitoring of electrocardiogram waveforms 846

Strachan I.
OBS Medical Co. Ltd, Abingdon, Oxon, United Kingdom

Аннотация

Novel algorithms for dynamic monitoring of electrocardiogram waveforms are presented. Their objective is to measure drug-induced changes in cardiac rhythm, principally by means of the QT-interval.

Ключевые слова: Electrocardiogram waveforms, Hidden Markov models

Рубрика: Научная жизнь

Тип: научная статья

Ссылка для цитирования

Фрагмент статьи

In this paper we present two novel algorithms for dynamic monitoring of electrocardiogram waveforms (ECG). The objective of the algorithms is to measure drug-induced changes in cardiac rhythm, princi­pally by means of the QT-interval – the time between the onset of ventricular depolarisation and the end ventricular repolarisation. This is a recognised biomarker which may indicate increased risk of car­diac arrhythmia, which may arise if the action of a drug prolongs this interval.

Литература
  1. Kass R. S. and Moss A. J., “Long QT syndrome: novel insights into the mechanisms of cardiac ar­rhythmias”, J Clin Invest, 2003, Vol 112, No 6, pp 810–815.
  2. “International Conference on Harmonisation. Guidance on E14 clinical evaluation of QT/QTc in­terval prolongation and proarrhythmic potential for non-antiarrhythmic drugs; availability”, Notice Fed Regist, 2005, Vol 70, No 202, pp 61134-61135.
  3. Rabiner L. R., “A tutorial on hidden Markov models and selected applications in speech recogni­tion”, Proceedings of the IEEE, 1989, Vol 77, No 2, pp 257–286.
  4. Hughes N. P., “Probabilistic models for automated ECG interval analysis”, DPhil thesis, Universi­ty of Oxford, 2006.
  5. Andrew J. Viterbi, “Error bounds for convolutional codes and an asymptotically optimum decod­ing algorithm”, IEEE Transactions on Information Theory, Vol 13, No 2, pp 260–269, April 1967.
  6. Shensa M.J., “The discrete wavelet transform: wedding the a trous and Mallat algorithms”, IEEE Trans. Signal Processing, 1992, Vol 40, pp 2464–2482.
  7. Hughes N.P. and Tarassenko L., “Probabilistic models for automated ECG interval analysis”, Submitted to: IEEE Trans. Biomedical Engineering.
  8. Sarapa N., Morganroth J., Couderc J.P. et al, “Electrocardiographic identification of drug-induced QT prolongation: Assessment by different recording and measurement methods”, A.N.E., 2004, Vol 9, No 1, pp 48–57.
  9. Holmes J. R. and Holmes W. Speech Synthesis and Recognition (2nd Edition), Taylor and Francis, 2001.
  10. Wong D., Strachan I., Tarassenko L., “Visualisation of high-dimensional data for very large data sets”, Proc. 25th International Conference on Machine Learning – Workshop on Machine Learning for Health Care Applications (Helsinki, Finland, 2008).
 
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