Signal analysis method

Surgery – Diagnostic testing – Cardiovascular

Reexamination Certificate

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C600S509000

Reexamination Certificate

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07941209

ABSTRACT:
Improvement in the reliability of segmentation of a signal, such as an ECG signal, is achieved through the use of duration constraints. The signal is analysed using a hidden Markov model. The duration constraints specify minimum allowed durations for specific states of the model. The duration constraints can be incorporated either in the model itself or in a Viterbi algorithm used to compute the most probable state sequence given a conventional model. The derivation of a confidence measure from the model can be used to assess the quality and robustness of the segmentation and to identify any signals for which the segmentation is unreliable, for example due to the presence of noise or abnormality in the signal.

REFERENCES:
patent: 5663929 (1997-09-01), Pavone et al.
patent: 5737489 (1998-04-01), Chou et al.
patent: 5778881 (1998-07-01), Sun et al.
patent: 2002/0049593 (2002-04-01), Shao
patent: 2347253 (2000-08-01), None
International Search Report for PCT/GB2005/001747 mailed Jan. 19, 2006.
Rabiner, “Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, Proceedings of the IEEE, 77:257-286 (1989).
Hughes et al., “Markov Models for Automated ECG Interval Analysis”, Advances in Neural Information Processing Systems 16, MIT Press, (2004).
Thoraval et al., “Continuously Variable Durations Hidden Markov Models for ECG Segmentation”, Proceedings IEEE-EMBS, pp. 529-530 (1992).
Ghahramani et al., “Factorial Hidden Markov Models”, Machine Learning, vol. 29(2) (1997).
Freund et al., “Regression Analysis”, Academic Press, Chapter 2 only (1998).
Hughes et al., “Automated QT Interval Analysis with Confidence Measures”, Computers in Cardiology, 31:765-768 (2004).
Coast et al., “An Approach to Cardiac Arrhythmia Analysis Using Hidden Markov Models”, IEEE Transactions on Biomedical Engineering, vol. 37(9):826-835 (1990).
Ajmera et al., “Speech/Music Segmentation Using Entropy and Dynamism Features in a HMM Classification Framework”, Speech Communication, vol. 40(3):351-363 (2003).
Laurila, “Noise Robust Speech Recognition with State Duration Constraints”, IEEE International Conference on Munich, vol. 2:871-874 (1997).
Gupta et al, “Using Phoneme Duration and Energy Contour Information . . . ”, Speech Processing 2, VLSI, Underwater Signal Processing, Toronto, May 14-17, 1991, International Conference on Acoustics, Speech & Signal Processing, ICASSP, New York, IEEE, U.S., vol. 2 Conf. 16, Apr. 14, 1991, pp. 341-344; XP010043891.
Vaseghi, “State duration modelling in hidden Markov models”, Signal Processing, Elsevier Science Publishers B.V. Amsterdam, NL, vol. 41, No. 1, Jan. 1995, pp. 31-41; XP004014180.
Kim et al, “HMM with Global Path Constraint in Viterbi Decoding for Isolated Word Recognition”, Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Speech Processing 1. Adelaide, vol. 1, Apr. 19, 1994, pp. I-605; XP000529435.
Burshtein, “Robust Parametric Modeling of Durations in Hidden Markov Models”, IEEE Transactions on Speech and Audio Processing, IEEE Service Center, New York, vol. 4, No. 3, May 1996, pp. 240-242, XP000800777.
Jiang, “Confidence Measures for Speech Recognition: A survey”, Speech Communication, Elsevier Science Publishers, Amsterdam, NL, vol. 45, No. 4, Apr. 2005, pp. 455-470, XP004823829.

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