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.
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
“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.
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.
Hughes N. P., “Probabilistic models for automated ECG interval
analysis”, DPhil thesis, University of Oxford, 2006.
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.
Shensa M.J., “The discrete wavelet transform: wedding the a trous
and Mallat algorithms”, IEEE Trans. Signal Processing, 1992, Vol 40, pp
Hughes N.P. and Tarassenko L., “Probabilistic models for automated
ECG interval analysis”, Submitted to: IEEE Trans. Biomedical Engineering.
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.
Holmes J. R. and Holmes W. Speech Synthesis and Recognition
(2nd Edition), Taylor and Francis, 2001.
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).