Abstract
The surface electroenterogram (EEnG) is a non-invasive method of studying myoelectrical bowel activity. However, surface EEnG recordings are contaminated by cardiac activity, respiratory and motion artifacts, and other sources of interference. The aim of this work is to remove the respiration artifact and the very low frequency components from surface EEnG by means of empirical mode decomposition (EMD). Eleven recording sessions were carried out on canine model. Several parameters were calculated before and after the application of the method: signal-to-interference ratio (S/I ratio) and the attenuation level of the signal and of interference. The results show that the S/I ratio was significantly higher after the application of the method (3.68 ± 5.54 dB vs. 10.45 ± 3.65 dB), the attenuation level of signal and of interference is −0.49 ± 0.80 dB versus −7.26 ± 5.42 dB, respectively. Therefore, EMD could be a useful aid in identifying the intestinal slow wave and in removing interferences from EEnG recordings.
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Akin A, Sun HH (1999) Time–frequency methods for detecting spike activity of stomach. Med Biol Eng Comput 37:381–390
Amaris MA, Sanmiguel CP, Sadowski DC, Bowes KL, Mintchev MP (2002) Electrical activity from colon overlaps with normal gastric electrical activity in cutaneous recordings. Dig Dis Sci 47:2480–2485
Antrade AO, Nasuto S, Kyberd P, Sweeney-Reed CM, Van Kanijn FR (2006) EMG signal filtering based on empirical mode decomposition. Biomed Signal Process Control 1:44–55
Bradshaw LA, Allos SH, Wikswo JP, Richards WO (1997) Correlation and comparison of magnetic and electric detection of small intestinal electrical activity. Am J Physiol Gastrointest Liver Physiol 35:G1159–G1167
Bradshaw LA, Richards WO, Wikswo JP (2001) Volume conductor effects on the spatial resolution of magnetic fields and electric potentials from gastrointestinal electrical activity. Med Biol Eng Comput 39:35–43
Byrne KG, Quigley EMM (1997) Antroduodenal manometry: an evaluation of an emerging methodology. Dig Dis 15:53–63
Camilleri M, Hasler WL, Parkman HP, Quigley EMM, Soffer E (1998) Measurement of gastrointestinal motility in the GI laboratory. Gastroenterology 115:747–762
Chen JD, Lin Z (1993) Adaptive cancellation of the respiratory artifact in surface recording of small intestinal electrical activity. Comput Biol Med 23:497–509
Chen JD, Mccallum RW (1994) Electrogastrography, principles and applications. Raven Press, New York
Chen JD, Schirmer BD, Mccallum RW (1993) Measurement of electrical-activity of the human small-intestine using surface electrodes. IEEE Trans Biomed Eng 40:598–602
Cordova-Fraga T, Carneiro AAO, de Araujo DB, Oliveira RB, Sosa M, Baffa O (2005) Spatiotemporal evaluation of human colon motility using three-axis fluxgates and magnetic markers. Med Biol Eng Comput 43:712–715
Donck LV, Lammers WJEP, Moreaux B, Smets D, Voeten J, Vekemans J, Schuurkes JAJ, Coulie B (2006) Mapping slow waves and spikes in chronically instrumented conscious dogs: implantation techniques and recordings. Med Biol Eng Comput 44:170–178
Echeverria JC, Crowe JA, Woolfson MS, Hayes-Gill BR (2001) Application of empirical mode decomposition to heart rate variability analysis. Med Biol Eng Comput 39:471–479
Garcia-Casado J, Martinez-de-Juan JL, Meseguer M, Ponce JL (2004) Stationarity study of the myoelectrical signal recorded from small bowel. In: Proc Ann Int Conf of the IEEE EMBS, San Francisco
Garcia-Casado J, Martinez-de-Juan JL, Ponce JL (2005) Noninvasive measurement and analysis of intestinal myoelectrical activity using surface electrodes. IEEE Trans Biomed Eng 52:983–991
Garcia-Casado J, Martinez-de-Juan JL, Ponce JL (2006) Adaptive filtering of ECG interference on surface EEnGs based on signal averaging. Physiol Meas 27:509–527
Gordon AD (1987) A review of hierarchical-classification. J R Stat Soc Ser A Stat Soc 150:119–137
Huang NE, Shen Z, Long SR, Wu MLC, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond A Mat 454:903–995
Huang NE, Wu MLC, Long SR, Shen SSP, Qu WD, Gloersen P, Fan KL (2003) A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc Lond A Mat 459:2317–2345
Irimia A, Bradshaw LA (2005) Artifact reduction in magnetogastrography using fast independent component analysis. Physiol Meas 26:1059–1073
Liang H (2001) Adaptive independent component analysis of multichannel electrogastrograms. Med Eng Phys 23:91–97
Liang J, Cheung JY, Chen JDZ (1997) Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks. Ann Biomed Eng 25:850–857
Liang H, Lin Z, Mccallum RW (2000) Artifact reduction in electrogastrogram based on empirical mode decomposition method. Med Biol Eng Comput 38:35–41
Lin ZY, Chen JDZ (1994) Recursive running DCT algorithm and its application in adaptive filtering of surface electrical recording of small-intestine. Med Biol Eng Comput 32:317–322
Martinez-de-Juan JL, Saiz J, Meseguer M, Ponce JL (2000) Small bowel motility: relationship between smooth muscle contraction and electroenterogram signal. Med Eng Phys 22:189–199
Mintchev MP, Bowes KL (1996) Extracting quantitative information from digital electrogastrograms. Med Biol Eng Comput 34:244–248
Quigley EM (1996) Gastric and small intestinal motility in health and disease. Gastroenterol Clin North Am 25:113–145
Rilling G, Flandrin P, and Goncalves P (2003) On empirical mode decomposition and its algorithms. In: IEEE-Eurasip workshop on nonlinear signal and image processing NSIP-03 Grado (I), Italy
Salisbury JI, Sun Y (2004) Assessment of chaotic parameters in nonstationary electrocardiograms by use of empirical mode decomposition. Ann Biomed Eng 32:1348–1354
Summers RW, Cramer J, Flatt AJ (1982) Computerized analysis of spike burst activity in the small-intestine. IEEE Trans Biomed Eng 29:309–314
Verhagen MAMT, Van Schelven LJ, Samsom M, Smout AJPM (1999) Pitfalls in the analysis of electrogastrographic recordings. Gastroenterology 117:453–460
Wang ZS, Cheung JY, Chen JDZ (1999) Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network. Med Biol Eng Comput 37:80–86
Weisbrodt NW (1987) Motility of the small intestine. In: Johnson LR (ed) Physiology of the gastrointestinal tract (vol 1). Raven Press, New York
Acknowledgments
The authors thank J. Bertelli, J. L. Guardiola, Dr. C. Vila and the Veterinarian Unit of the Research Centre of ‘La Fe’ University Hospital (Valencia, Spain), where surgical interventions and recording sessions were carried out, and the R + D + I Linguistic Assistance Office at the UPV for their help in revising this paper. This work was supported by the Instituto Carlos III (FIS-03/0432), by the Universidad Politécnica de Valencia under Programa de apoyo a la investigación y el desarrollo de la UPV, and by Conselleria de Universitat, Educació y Ciència de la Generalitat Valenciana.
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Ye, Y., Garcia-Casado, J., Martinez-de-Juan, J.L. et al. Empirical mode decomposition: a method to reduce low frequency interferences from surface electroenterogram. Med Bio Eng Comput 45, 541–551 (2007). https://doi.org/10.1007/s11517-007-0189-7
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DOI: https://doi.org/10.1007/s11517-007-0189-7