Electrogastrographic examination (EGG) is a noninvasive method for an investigation of a stomach slow wave propagation. The typical range of frequency for EGG signal is from 0.015 to 0.15 Hz or (0.015-0.3 Hz) and the signal usually is captured with sampling frequency not exceeding 4 Hz. In this paper a new approach of method for recording the EGG signals with high sampling frequency (200 Hz) is proposed. High sampling frequency allows collection of signal, which includes not only EGG component but also signal from other organs of the digestive system such as the duodenum, colon as well as signal connected with respiratory movements and finally electrocardiographic signal (ECG). The presented method allows improve the quality of analysis of EGG signals by better suppress respiratory disturbance and extract new components from high sampling electrogastrographic signals (HSEGG) obtained from abdomen surface. The source of the required new signal components can be inner organs such as the duodenum and colon. One of the main problems that appear during analysis the EGG signals and extracting signal components from inner organs is how to suppress the respiratory components. In this work an adaptive filtering method that requires a reference signal is proposed. In the present research, the respiratory component is obtained from non standard ECG (NSECG) signal. For purposes of this paper non standard ECG (namely NSECG) is used, because ECG signal was recorded by other than the standard electrodes placement on the surface of the abdomen. The electrocardiographic derived respiration signal (EDR) is extracted using the phenomena of QRS complexes amplitude modulation by respiratory movements. The main idea of extracting the EDR signal from electrocardiographic signal is to obtain the modulating signal. Adaptive filtering is done in the discrete cosine transform domain. Next the resampled HSEGG signal with attenuated respiratory components is low pass filtered and as a result the extended electrogastrographic signals, included EGG signal and components from other inner organs of digestive system is obtained. One of additional features of the proposed method is a possibility to obtain simultaneously recorded signals, such as: non-standard derivation of ECG, heart rate variability signal, respiratory signal, and EGG signal that allow investigating mutual interferences among internal human systems.

Department of Biomedical Engineering Brno University of Technology 12 Technicka Street 61200 Brno Czech Republic

Department of Biosensors and Biomedical Signals Processing Faculty of Biomedical Engineering Silesian University of Technology 40 Roosevelt'a Street 44 800 Zabrze Poland

Division of Biomedical Electronics Institute of Electronics Silesian University of Technology 16 Akademicka Street 44 100 Gliwice Poland

International Clinical Research Center Center of Biomedical Engineering St Anne's University Hospital Brno Brno Czech Republic

See more in PubMed

Parkman HP, Hasler WL, Barnett JL, Eaker EY. Electrogastrography: a document prepared by the gastric section of the American Motility Society Clinical GI Motility Testing Task Force. Neurogastroenterol Motil. 2003;15:89–102. doi: 10.1046/j.1365-2982.2003.00396.x. PubMed DOI

Koch KL, Stern RM. Handbook of electrogastrography. New York: Oxford University Press; 2004.

Jonderko K, Kasicka-Jonderko A, Krusiec-Swidergoł B, Dzielicki M, Strój L, Doliński M, et al. How reproducible is cutaneous electrogastrography? An in-depth evidence-based study. Neurogastroenterol Motil. 2005;17:800–9. doi: 10.1111/j.1365-2982.2005.00707.x. PubMed DOI

Medtronic A/S . Polygram NetTM Reference Manual. Denmark: Skovlunde; 2002.

Yin J, Chen JDZ. Electrogastrography: methodology, validation and applications. J Neurogastroenterol Motil. 2013;19(1):5–17. doi: 10.5056/jnm.2013.19.1.5. PubMed DOI PMC

Riezzo G, Russo F, Indrio F. Electrogastrography in adults and children: the strength, pitfalls, and clinical significance of the cutaneous recording of the gastric electrical activity. BioMed Res Int. 2013;2013:14. doi: 10.1155/2013/282757. PubMed DOI PMC

Yasuyuki M, Hiroki T. Form and its nonlinear analysis for the use of electrogastrogram as a gastrointestinal motility test. Forma. 2011;26:39–50.

Travaglini A, Lamberti C, DeBie J, Ferri M. Respiratory signal derived from eight-lead ECG. Comput Cardiol. 1998;25:65–68.

Bailón R, Sörnmo L, Laguna P. A robust method for ECG-based estimation of the respiratory frequency during stress testing. IEEE Trans Biomed Eng. 2006;53:1273–1285. doi: 10.1109/TBME.2006.871888. PubMed DOI

Cysarz D. Comparison of respiratory rates derived from heart rate variability, ECG amplitude, and nasal/oral airflow. Ann Biomed Eng. 2008;36:2085–2094. doi: 10.1007/s10439-008-9580-2. PubMed DOI

Mazzanti B, Lambertil C, de Bie J. Validation of an ECG-derived respiration monitoring method. In: Proceedings of computers in cardiology. IEEE; 2003. p. 613–6.

Park SB, Noh YS, Park SJ, Yoon HR. An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation. Med Biol Eng Comput. 2008;46:147–158. doi: 10.1007/s11517-007-0302-y. PubMed DOI PMC

Pan J, Tompkins W. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32:230–236. doi: 10.1109/TBME.1985.325532. PubMed DOI

Moody GB, Mark RG, Zoccola A, Mantero S. Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol. 1985;12:113–116 (Washington, DC: IEEE Computer Society Press). http://physionet.org/physiotools/edr/cic85/.

Chen J, McCallum RW. Electrogastrogram: measurement, analysis and prospective applications. Med Biol Eng Comput. 1991;29:339–350. doi: 10.1007/BF02441653. PubMed DOI

Liang H. Extraction of gastric slow waves from electrogastrograms: combining independent component analysis and adaptive signal enhancement. Med Biol Eng Comput. 2005;43:245–251. doi: 10.1007/BF02345962. PubMed DOI

Widrow B, Glover JR, Jr, McCool JM, Kaunitz J, Williams CS, Hearn RH, et al. Adaptive noise cancelling: principles and applications. Proc IEEE. 1975;63(12):1692–716. doi: 10.1109/PROC.1975.10036. DOI

Butterweck HJ. Traveling-wave model of long LMS filters. In: Haykin S, Widrow B, editors. Least-mean-square adaptive filters. Hoboken, NJ: Wiley; 2003.

Hamilton JW, Bellahsene BE, Reichelderfer M, Webster JG, Bass P. Human electrogastrograms: comparison of surface and mucosal recordings. Dig Dis Sci. 1986;31(1):33–39. doi: 10.1007/BF01347907. PubMed DOI

Komorowski D, Pietraszek S. The noise influence on determination dominant frequencies of EGG signal. In: Proceedings of the 31th annual international IEEE EMBS conference, 2–6 Sep 2009, Minneapolis, Minnesota, USA; 2009. PubMed

Pietraszek S, Komorowski D. The simultaneous recording and analysis both EGG and HRV signals. In: Proceedings of the 31th annual international IEEE EMBS conference, 2–6 Sep 2009, Minneapolis, Minnesota, USA; 2009. PubMed

Komorowski D, Pietraszek S. Preprocessing for spectral analysis of electrogastrogram. In: World Congress on Medical Physics and Biomedical Engineering 2009 (WC2009), Munich, Germany, 7–12 Sep 2009, Issue on CD; 2009.

Amaris MA, Sanmiguel CP, Sadowski DC, Bowes KL, Mintchev MP. Electrical activity from colon overlaps with normal gastric electrical activity in cutaneous recordings. Dig Dis Sci. 2002;47(11):2480–2485. doi: 10.1023/A:1020503908304. PubMed DOI

Liang J, Chen JDZ. What can be measured from surface electrogastrography. Dig Dis Sci. 1997;42(7):1331–1343. doi: 10.1023/A:1018869300296. PubMed DOI

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals