A method for the automated detection of venous gas bubbles in humans using empirical mode decomposition

Annals of Biomedical Engineering

Volumn 33, Issue 10, 2005, Pages 1411-1421

  Chappell, M A ^a   Payne, S J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Decompression sickness; Doppler ultrasound; Hilbert Huang transform

Indexed keywords

AUTOMATION; BUBBLES (IN FLUIDS); DOPPLER EFFECT; ULTRASONICS;

DECOMPRESSION SICKNESS; DOPPLER ULTRASOUND; HILBERT-HUANG TRANSFORM;

BIOMEDICAL ENGINEERING;

AIR EMBOLISM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BLOOD; COMPUTER ASSISTED DIAGNOSIS; DECOMPRESSION SICKNESS; DIVING; DOPPLER FLOWMETRY; ECHOGRAPHY; GAS; HUMAN; METABOLISM; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VEIN;

ARTIFICIAL INTELLIGENCE; DECOMPRESSION SICKNESS; DIVING; EMBOLISM, AIR; GASES; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY, DOPPLER; VEINS;

EID: 27244455211     PISSN: 00906964     EISSN: 15739686     Source Type: Journal    
DOI: 10.1007/s10439-005-6045-8     Document Type: Article

References (32)

1. Belcher, E. O. Quantification of bubbles formed in animals and man during decompression. IEEE Trans. Biomed. Eng. 27:330-338, 1980.
2. Chan, B. C. B., F. H. Y. Chan, F. K. Lam, P. Lui, and P. W. F. Poon. Fast detection of venous air embolism in Doppler heart sounds using the wavelet transform. IEEE Trans. Biomed. Eng. 44:237-246, 1997.
3. Chappell, M. A., and S. J. Payne. The automated extraction of the characteristics of blood cell and bubble movement from Doppler ultrasound recordings. Undersea Hyperb. Med. 31:341, 2004.
4. Cullinane, M., R. Dittrich, Z. Kaposzta, H. S. Markus, G. Reid, R. Dittrich, D. W. Droste, R. Ackerstaff, V. Babikian, D. Grossett, M. Siebler, L. Valton, and H. S. Markus. Evaluation of new online automated embolic signal detection algorithm, including comparison with panel of international experts. Stroke 31:1335-1341, 2000.
5. 'Evans, D. H., and W. N. McDicken. Doppler Ultrasound Physics, Instrumentation and Signal Processing. Wiley, Chichester, UK, 2000.
6. Fan, L., D. H. Evans, and A. R. Naylor. Automated embolus identification using a rule-based expert system. Ultrasound Med. Biol. 27:1065-1077, 2001.
7. Flandrin, P., G. Rilling, and P. Gonçalvés. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett. 11:112-114, 2004.
8. Gibby, G. L. Real-time automated computerized detection of venous air emboli in dogs. J. Clin. Monit. 9:354-363, 1993.
9. Gibby, G. L., and M. D. Ghani. Computer-assisted Doppler monitoring to enhance detection of air emboli. J. Clin. Monit. 4: 64-73, 1988.
10. Hamilton, P., and W. Tompkins. Quantitative investigation of QRS detection rules using the MIT/BIH arrythmia database. IEEE Trans. Biomed. Eng. 33:1157-1165, 1986.
11. Huang, N. E., C. W. Man-Li, S. R. Long, S. P. Shen, Q. Wendong, P. Gloersen, and K. L. Fan. A confidence limit for the empirical mode decomposition and hilbert spectral analysis. Proc. R. Soc. Land. A Math. Phys. Eng. Sci. 459:2317-2345, 2003.
12. Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the hilbert spectrum for nonlinear time series analysis. Proc. R. Soc. Lond. A 454:903-995, 1998.
13. Krongold, B. S., A. M. Sayeed, M. A. Moehring, J. A. Ritcey, M. P. Spencer, and D. L. Jones. Time-scale detection of microemboli in flowing blood with Doppler ultrasound. IEEE Trans. Biomed. Eng. 46:1081-1089, 1999.
14. Markus, H., M. Cullinane, and G. Reid. Improved automated detection of embolic signals using a novel frequency filtering approach. Stroke 30:1610-1615, 1999.
15. Markus, H., A. Loh, and M. M. Brown. Computerized detection of cerebral emboli and discrimination from artifact using Doppler ultrasound. Stroke 24:1667, 1993.
16. Marroni, A., P. B. Bennett, F. J. Cronje, R. Cali-Corleo, P. Germonpre, M. Pieri, C. Bonuccelli, and C. Balestra. A deep stop during decompression from 82 fsw (25 m) significantly reduces bubbles and fast tissue gas tensions. Undersea Hyperb. Med. 31:233-243, 2004.
17. Marroni, A., and R. Cali Corleo. Project SAFE DIVE - a preliminary report. In: XXI Annual Meeting of the European Underwater and Barometric Society, 1995.
18. Marroni, A., R. Cali Corleo, C. Balestra, E. Voellm, and M. Pieri. Incidence of asymptomatic circulating venous gas emboli in unrestricted, uneventful recreational diving. DAN Europe's project SAFE DIVE first results. In: XXVI Annual Scientific Meeting of the European Underwater and Barometric Society, Malta, 1996.
19. Marroni, A., R. Cali Corleo, and P. Denoble. Understanding the safety of recreational diving. DAN Europe's project SAFE DIVE phase I: Fine tuning of the field research engine and methods. In: International Joint Meeting on Hyperbaric and Underwater Medicine, Milano, 1996.
20. Munts, A. G., R. G. A. Ackerstaff, W. H. Mess, L. Walda, and E. F. Bruggemans. Feasibility and reliability of on-line automated microemboli detection after carotid endarterectomy. A transcranial Doppler study. Eur. J. Vasc. Endovasc. Surg. 25:262-266, 2003.
21. Nishi, R. Y. The scattering and absorption of sound waves by a gas bubble 0 in a viscous liquid. Acoustica 33:173-179, 1975.
22. Nishi, R. Y., A. O. Brubakk, and O. S. Eftedal. Bubble detection. In: Bennett and Elliott's Physiology and Medicine of Diving, edited by A. O. Brubakk and T. S. Neuman. Saunders, London, UK, 2003, pp. 501-529.
23. Nishi, R. Y., and B. C. Eatock. The role of ultrasonic bubble detection in table validation. In: Validation of Decompression Tables, edited by H. R. Schreiner and R. W. Hamilton. Undersea and Hyperbaric Medical Society, Bethesda, 1989, pp. 133-137.
24. Payne, S. J., and M. A. Chappell. Automated determination of bubble grades from doppler ultrasound recordings. Aviation Space Environ. Med. 76:771-777, 2005.
25. Roy, E., S. Montresor, P. Abraham, and J.-L. Saumet. Spectrogram analysis of arterial Doppler signals for off-line automated HITS detection. Ultrasound Med. Biol. 25:349-359, 1999.
26. Sawatzky, K. D., and R. Y. Nishi. Assessment of inter-rater agreement on the grading of intravascular bubble signals. Undersea Biomed. Res. 18:373-396, 1991.
27. Schneck, D. J. An outline of cardiovascular structure and function. In: The Biomedical Engineering Handbook, edited by J. D. Bronzino. CRC press and IEEE Press, Boca Raton, Fia., 2000, pp. 1-12.
28. Siebler, M., G. Rose, M. Sitzer, A. Bender, and H. Steinmetz. Real-time identification of cerebral microemboli with US feature detection by a neural network. Radiology 192:739, 1994.
29. Strong, K., D. W. Westenskow, P. G. Fine, and J. A. Orr. A preliminary laboratory investigation of air embolus detection and grading using an artificial neural network. Int. J. Clin. Monit. Comput. 14:103-107, 1997.
30. Sutherland, J. Automated cardiac monitoring using holographic/ quantum neural technology. PC AI 13:20-22, 1999.
31. Van Zuilen, E. V., W. H. Mess, C. Jansen, I. Van Der Tweel, J. Van Gijn, and R. G. A. Ackerstaff. Automatic embolus detection compared with human experts: A Doppler ultrasound study. Stroke 27:1840, 1996.
32. Wu, Z., and N. E. Huang. A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 460:1597-1611, 2004.