Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: A bioacoustics application in gastrointestinal-motility monitoring

Computers in Biology and Medicine

Volumn 37, Issue 4, 2007, Pages 438-462

  Dimoulas, C ^a   Kalliris, G ^a   Papanikolaou, G ^a   Kalampakas, A ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b Neurology Department   (Greece)

Author keywords

Bowel sounds; De noising; Fractal dimension; Gastrointestinal motility; Gastrointestinal phonography; Gastrointestinal sounds; Long term processing; Signal detection; Wavelets

Indexed keywords

FRACTALS; GASTROENTEROLOGY; MEDICAL APPLICATIONS; PHONOGRAPH RECORDS; WAVELET TRANSFORMS;

BOWEL SOUNDS; DE-NOISING; FRACTAL DIMENSIONS; GASTROINTESTINAL MOTILITY; GASTROINTESTINAL PHONOGRAPHY; GASTROINTESTINAL SOUNDS; LONG-TERM PROCESSING;

SIGNAL DETECTION;

ACOUSTICS; ARTICLE; ARTIFACT; FRACTAL ANALYSIS; GASTROINTESTINAL MOTILITY; INTESTINE SOUND; MULTICHANNEL RECORDER; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL DETECTION; SIGNAL NOISE RATIO; TOPOGRAPHY;

ALGORITHMS; ARTIFACTS; CIRCADIAN RHYTHM; COMPUTER GRAPHICS; FASTING; FRACTALS; GASTROINTESTINAL MOTILITY; HUMANS; MATHEMATICAL COMPUTING; MONITORING, AMBULATORY; POSTPRANDIAL PERIOD; REFERENCE VALUES; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; SOUND SPECTROGRAPHY;

EID: 33846409816     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2006.08.013     Document Type: Article

References (67)

1. Koymik M.K., Güler I., Dizibüyük A., and Akin M. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput. Biol. Med. 35 (2005) 603-616
2. Zhang Z., Kawabata H., and Liu Z. Electroencephalogram analysis using fast wavelet transform. Comput. Biol. Med. 31 (2001) 429-440
3. Erçelebi E. Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Comput. Biol. Med. 34 (2004) 479-493
4. F. Dirgenali, S. Kara, S. Okkesim, Estimation of wavelet and short-time Fourier transform sonograms of normal and diabetic subjects' electrogastrogram, Comput. Biol. Med., (2006), in press, doi:10.1016/j.compbiomed.2005.07.05
5. Shyu L.-Y., Lin Y.-S., Liu C.-P., and Hu W.-C. The detection of impedance cardiogram characteristic points using wavelet transform. Comput. Biol. Med. 34 (2004) 165-175
6. Guido R.C., Pereira J.C., Fonseca E., Vieira L.S., and Sanchez F.L. Trying different wavelets on the search for voice disorders sorting. Proceedings of the 37th IEEE SSST-International Southeastern Symposium on System Theory vol. 1 (2005), Tuskegee, AL, IEEE Press, New York 459-499
7. Fonseca E., Guido R.C., Silvestre A.C., and Pereira J.C. Discrete wavelet transform and support vector machine applied to pathological voice signals identification. Proceedings of the Seventh IEEE International Symposium on Multimedia (ISM'05) 0-7695-2489-3/05 (2005), IEEE Press, New York
8. Coifman R., and Wickerhauser M.V. Adapted waveform 'de-noising' for medical signals and images. IEEE Eng. Med. Biol. 14 5 (1995) 578-586
9. Hadjileontiadis L.J., and Panas S.M. A wavelet-based reduction of heart sound noise from lung sounds. Int. J. Med. Inf. 52 1-3 (1998) 183-190
10. Hadjileontiadis L.J., and Panas S.M. Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter. IEEE Trans. Biomed. Eng. 44 12 (1997) 1269-1281
11. Hadjileontiadis L.J. Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part I: methodology. IEEE Trans. Biomed. Eng. 52 6 (2005) 1143-1148
12. Hadjileontiadis L.J. Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part II: application results. IEEE Trans. Biomed. Eng. 52 6 (2005) 1050-1064
13. Hadjileontiadis L.J., Liatsos C.N., Mavrogiannis C.C., Rokkas T.A., and Panas S.M. Enhancement of bowel sounds by wavelet-based filtering. IEEE Trans. Biomed. Eng. 47 7 (2000) 876-886
14. C. Dimoulas, G. Kalliris, G. Papanikolaou, A. Kalampakas, Novel wavelet domain Wiener filtering de-noising techniques: application to bowel sounds captured by means of abdominal surface vibrations, Biomed. Signal Process. Control, (2006), in press.
15. C. Dimoulas, G. Kalliris, G. Papanikolaou, A. Kalampakas, Abdominal sounds pattern classification using advanced signal processing and artificial intelligence, in: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2003), Vienna, February 2003, pp. 71-82.
16. R. Ranta, C. Heinrich, V. Louis-Dorr, D. Wolf, F. Guillemin, Wavelet-based bowel sounds denoising, segmentation and characterization, in: Proceedings of 23rd Annual International Conference IEEE EMBS, Istanbul, Turkey, October 25-28, 2001.
17. Cohen L. Time-frequency distributions-a review. Proc. IEEE 77 7 (1989) 941-981 (invited paper)
18. Rioul Ol., and Vetterli M. Wavelets and signal processing. IEEE Signal Process. Mag. 8 4 (1991) 14-38
19. Unser M., and Blu T. Wavelet theory demistified. IEEE Trans. Signal Process. 51 2 (2003) 470-483
20. Painter T., and Spanias A. Perceptual coding of digital audio. Proc. IEEE 88 4 (2000) 451-515
21. Guido R.C., Slaets J.F.W., Köberle R., Batista Almeida L.O., and Carlos Pereira J. A new technique to construct a wavelet transform matching a specified signal with applications to digital, real time, spike and overlap pattern recognition. Digital Signal Process. 16 (2006) 24-44
22. Rioul Ol., and Duhamel P. Fast algorithms for discrete and continuous wavelets transforms. IEEE Trans. Inf. Theory 38 2 (1992) 569-586
23. Donoho D.L. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41 3 (1995) 613-627
24. Quinquis A. A few practical applications of wavelet packets. Digital Signal Process. 8 (1998) 49-60
25. Cannon W.B. Auscultation of the rhythmic sounds produced by the stomach and intestine. Am. J. Physiol. 13 (1905) 339-353
26. Dalle D., Devroede G., Thibault R., and Perrault J. Computer analysis of BS. Comput. Biol. Med. 4 (1975) 247-256
27. Campbell F.C., Storey B.E., and Cullen P.T. Surface vibration analysis (SVA): a new non-invasive monitor of gastrointestinal activity. Gut 30 (1989) 39-45
28. Gamer C., and Ehrenreich H. Non invasive topographic analysis of intestinal activity in man on the basis of acoustic phenomena. Res. Exp. Med. (Bed.) 189 2 (1989) 129-140
29. Yoshino H., Abe Y., Yoshino T., and Oshato K. Clinical application of spectral analysis of bowel sounds in intestinal obstruction. Dis. Colon. Rectum 33 9 (1990) 753-757
30. C. Pastiadis, G. Papanikolaou, Ch. Dimoulas, A. Kalampakas, Intestinal motility recording and analysis, in: Proceedings of the VIII Mediterranean Conference on Medical and Biological Engineering and Computing Medicon '98, 〈www.cs.ucy.ac.cy/med98/med98.htm〉, Lemesos, Cyprus, 1998.
31. Dimoulas Ch., Papanikolaou G., Kalliris G., and Pastiadis C. Computer aided systems for prolonged recording and analysis of human bowel sounds. J. Acoust. Soc. Am. 105 2 (1999) 1102 (abstract)
32. Tomomasa T., Morikawa A., Sandler R.H., Mansy H.A., Koneko H., Masahiko T., Hyman P.E., and Itoh Z. Gastrointestinal sounds and migrating motor complex in fasted humans. Am. J. Gastroenterol. 94 2 (1999) 374-381
33. Craine B., Silpa M., and O'Toole C. Computerized auscultation applied to irritable bowel syndrome. Dig. Dis. Sci. 44 9 (1999) 1887-1892
34. Craine B., Silpa M., and O'Toole C. Enterotachogram analysis to distinguish irritable bowel syndrome from Crohn's disease. Dig. Dis. Sci. 46 9 (2001) 1974-1979
35. Craine B.L., Silpa M.L., and O'Toole C.J. Two-dimensional positional mapping of gastrointestinal sounds in control and functional bowel syndrome patients. Dig. Dis. Sci. 47 (2002) 1290-1296
36. Hadjileontiadis L.J., and Rekanos I.T. Detection of explosive lung and bowel sounds by means of fractal dimension. IEEE Signal Process. Lett. 10 10 (2003) 311-314
37. Liatsos C., Hadjileontiadis L.J., Mavrogiannis C., Patch D., Panas S.M., and Burroughs A.K. Bowel sounds analysis: a novel non-invasive method for diagnosis of small-volume ascites. Dig. Dis. Sci. 48 (2003) 1630-1636
38. Vantrappen G., Janssens J., Coremans G., and Jian R. Gastrointestinal motility disorders. Dig. Dis. Sci. 31 9 (1983) 5S-25S
39. Weisbrodt N.W. Motility of the small intestine. In: Johnson L.R. (Ed). Physiology of the Gastrointestinal Tract. second ed. (1987), Raven, New York 631-663
40. Sharna S. Intestinal manometry to evaluate intestinal motor function. In: Read N.W. (Ed). Gastrointestinal Motility: Which test? vol. 18 (1989), Wrighton Biomedical Publishing Ltd. 157-166
41. Kellow J.E., Gill R.C., and Wingate D.L. Prolonged ambulant recordings of small motility demonstrate abnormalities in the irritable bowel syndrome. Gastroenterology 98 5 (1990) 1208-1218
42. Shibata Y., Toyoda S., Nimura Y., and Miyati M. Patterns of intestinal motility recovery during the early stage following abdominal surgery: clinical and manometric study. World J. Surg. 21 8 (1997) 806-809
43. Chen J., Schirmer B.D., and McCallum R.W. Measurement of electric activity of the human small intestine using surface electrodes. IEEE Trans. Biomed. Eng. 40 6 (1993) 598-602
44. Garcia-Casado J., Martinez-de-Juan J.L., and Ponce J.L. Non-invasive measurement and analysis of intestinal myoelectrical activity using surface electrodes. IEEE Trans. Biomed. Eng. 52 6 (2005) 983-991
45. Koenen R., and Pereira F. MPEG-7: a standardised description of audiovisual content. Signal Process: Image Commun. 16 1-2 (2000) 5-13
46. Special issue on MPEG-7, IEEE Trans. Circuits Syst. Video Technol. 11(6) (2001).
47. G. Kalliris, Ch. Dimoulas, G. Papanikolaou, Broad-band acoustic noise reduction using a novel frequency depended parametric Wiener filter: implementations using filter-bank, STFT and wavelet analysis/synthesis techniques, Audio Engineering Society Preprint, in: Proceedings of the 110th AES Convention, Amsterdam, May 2001 (Preprint 5382).
48. Li D., Sethi I.K., Dimitrova N., and McGee T. Classification of general audio data for content-based retrieval. Pattern Recognition Lett. 22 (2001) 533-544
49. Zhang T., and Jay Kuo C.-C. Audio content analysis for online audiovisual data segmentation and classification. IEEE Trans. Speech Audio Process. 9 4 (2001) 441-457
50. Lu L., Zhang H.-J., and Jiang H. Content analysis for audio classification and segmentation. IEEE Trans. Speech Audio Process. 10 7 (2002) 504-516
51. Li Y., Narayanan S., and Jay Kuo C.-C. Content-based movie analysis and indexing based on audio visual cues. IEEE Trans. Circuits Syst. Video Technol. 14 8 (2004) 1073-1085
52. Giannakis G.B., and Tsatsanis M.K. Signal detection and classification using matched filtering and higher order statistics. IEEE Trans. Acoust. Speech Signal Process. 38 7 (1990)
53. Zhang J.Q. An eigenvalue residuum-based criterion for detection of the number of sinusoids in white Gaussian noise. Digital Signal Process. 13 (2003) 275-283
54. Brüel and Kjær Sound and Vibration Measurement A/S, Basic Concepts of Sound, Lecture Note BA 7666-11, vol. 1, 1998. Available on-line: 〈www.bk.dk〉.
55. Brüel P.V., Pope J., and Zaveri H.K. In: Crocker M. (Ed). Acoustical measurements and instrumentation: introduction encyclopedia of acoustics vol. IV (1997), Wiley, New York 1839-1844 ISBN 0-471-80465-7
56. Esteller R., Vachtsevanos G., Echauz J., and Litt B. A comparison of waveform fractal dimension algorithms. IEEE Trans. Circuits Syst.-I: Fundam. Theory Appl. 48 2 (2001) 177-183
57. Katz M.J. Fractals and the analysis of waveforms. Comput. Biol. Med. 18 3 (1988) 145-156
58. Petrosian A. Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. Proceedings of the IEEE Symposium on Computer-Based Medical Systems (1995) 212-217
59. Badwal R.S.S. The application of fractal dimension to temporomandibular joint sounds. Comput. Biol. Med. 23 1 (1993) 1-14
60. Accardo P., and Mumolo E. An algorithm for the automatic differentiation between the speech of normals and patients with Friedreich's ataxia based on the short-term fractal dimension. Comput. Biol. Med. 28 (1998) 75-89
61. Shono H., Peng C.-K., Goldbergerb A.L., Shono M., and Sugimori H. A new method to determine a fractal dimension of non-stationary biological time-serial data. Comput. Biol. Med. 30 (2000) 237-245
62. Al-Akaidi M. Fractal Speech Processing (2004), Cambridge University Press, Cambridge ISBN: 0521814588
63. S. Fekkai, M. Al-Akaidi, J. Blackledge, A new speech synthesis based on fractal, XI European Signal Processing Conference EUSIPCO, vol. II, 2002, pp. 351-354.
64. Dubuc B., Quiniou J.F., Roques-Carmes C., Tricot C., and Zucker S.W. Evaluating the fractal dimension of profiles. Phys. Rev. A 39 3 (1989) 1500-1512
65. Dutta D., and Barat P. Fractal characterization of ultrasonic backscattered signals from single crystal and polycrystalline materials. J. Acoust. Soc. Am. 98 (1995) 938-942
66. Pittner S., and Kamarthi S.V. Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Trans. Pattern Anal. Mach. Intell. 21 1 (1999) 83-88
67. Torrence C., and Compo G.P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79 1 (1998) 61-78