Effect of electrode contact area on the information content of the recorded electrogastrograms: An analysis based on Rényi entropy and Teager-Kaiser Energy

• Autorzy: Alagumariappan P. , Krishnamurthy K. , Kandiah S. , Ponnuswamy M. J.

Identyfikatory

DOI

10.1515/pjmpe-2017-0007

• Języki publikacji: EN

Abstrakty

EN

Electrogastrograms (EGG) are electrical signals originating from the digestive system, which are closely correlated with its mechanical activity. Electrogastrography is an efficient non-invasive method for examining the physiological and pathological states of the human digestive system. There are several factors such as fat conductivity, abdominal thickness, change in electrode surface area etc, which affects the quality of the recorded EGG signals. In this work, the effect of variations in the contact area of surface electrodes on the information content of the measured electrogastrograms is analyzed using Rényi entropy and Teager-Kaiser Energy (TKE). Two different circular cutaneous electrodes with approximate contact areas of 201.14 mm2 and 283.64 mm2, have been adopted and EGG signals were acquired using the standard three electrode protocol. Further, the information content of the measured EGG signals were analyzed using the computed values of entropy and energy. Results demonstrate that the information content of the measured EGG signals increases by 6.72% for an increase in the contact area of the surface electrode by 29.09%. Further, it was observed that the average energy increases with increase in the contact surface area. This work appears to be of high clinical significance since the accurate measurement of EGG signals without loss in its information content, is highly useful for the design of diagnostic assistance tools for automated diagnosis and mass screening of digestive disorders.

Słowa kluczowe

EN

electrogastrography; biopotentials; cutaneous electrodes; Rényi entropy; Teager-Kaiser energy operator; information content

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2017
• Tom: Vol. 23, Iss. 2
• Strony: 37--42
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Alagumariappan P.

• Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai 600044, India

autor

Krishnamurthy K.

• Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai 600044, India

autor

Kandiah S.

• Shree Balaji Clinic, Ezhil Nagar, Selaiyur, Chennai 600073, India

autor

Ponnuswamy M. J.

• Anna University, Chennai 600025, India

Bibliografia

• [1] Swobodnik W, Soloway RS, Ditschuneit H (Eds). Gallstone disease: pathophysiology and therapeutic approaches. Springer Science & Business Media, 2012.
• [2] Siegel MA, Jacobson JJ, Braun RJ. Diseases of the Gastrointestinal Tract, Chapter 14. In: Burket's Oral Medicine: Diagnosis & Treatment. Greenberg MS, Glick M (Eds). BC Decker, Inc. 2003;389-406.
• [3] Neuman MR. The Electrical Engineering Handbook. Richard C. Dorf (Ed). CRC Press LLC, 2000.
• [4] Yin J, Chen JD. Electrogastrography: methodology, validation and applications. Journal of Neurogastroenterology and Motility. 2013;19(1):5-17.
• [5] Gopu G, Neelaveni R, Porkumaran K. Investigation of digestive system disorders using Electrogastrogram. Computer and Communication Engineering, 2008. ICCCE 2008. 201-205.
• [6] Borowska M. Entropy-based algorithms in the analysis of biomedical signals. Studies in Logic, Grammar and Rhetoric. 2015;43(1):21-32.
• [7] Rosso OA. Entropy changes in brain function. International Journal of Psychophysiology. 2007;64(1):75-80.
• [8] Ahmad SA, Chappell PH. Moving approximate entropy applied to surface electromyographic signals. Biomedical Signal Processing and Control. 2008 Jan 31;3(1):88-93.
• [9] Hassan M, Terrien J, Marque C, et al. Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Eng Phys. 2011;33(8):980-986.
• [10] Sun R, Song R, Tong KY. Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2014;22(5):1013-1019.
• [11] Bromiley PA, Thacker NA, Bouhova-Thacker E. Shannon entropy, Rényi entropy, and information. Statistics and Inf. Series (2004-004). 2004.
• [12] Castner DG, Ratner BD. Biomedical surface science: Foundations to frontiers. Surface Science. 2002;500(1-3):28-60.
• [13] Kvedalen E. Signal processing using the Teager energy operator and other nonlinear operators. Thesis. University of Oslo Department of Informatics. 2003.
• [14] Kasicka-Jonderko A, Jonderko K, Krusiec-Swidergol B, et al. Comparison of multichannel electrogastrograms obtained with the use of three different electrode types. J Smooth Muscle Res. 2006;42(2-3):89-101.
• [15] Taji B, Shirmohammadi S, Groza V, etc. Impact of skin–electrode interface on electrocardiogram measurements using conductive textile electrodes. IEEE Transactions on Instrumentation and Measurement. 2014;63(6):1412-1422.
• [16] Mintchev M, Stickel A, Bowes K. Impact of different electrode surface areas on validity of human electrogastrograms. Medical and Biological Engineering and Computing. 1997;35(1):66-68.
• [17] Mintchev MP, Bowes KL. Computer simulation of the effect of changing abdominal thickness on the electrogastrogram. Med Eng Phys. 1998;20(3):177-181.
• [18] Kim JH, Pullan AJ, Bradshaw LA, et al. Influence of body parameters on gastric bioelectric and biomagnetic fields in a realistic volume conductor. Physiol Meas. 2012;33(4):545-556.
• [19] 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;2013:282757.
• [20] Parkman HP, Hasler WL, Barnett JL, et al. Electrogastrography: a document prepared by the gastric section of the American Motility Society Clinical GI Motility Testing Task Force. Neurogastroenterol Motil. 2003;15(2):89-102.
• [21] Schrödinger E. What is life?: With mind and matter and autobiographical sketches. Cambridge University Press; 1992.
• [22] Alagumariappan P, Rajagopal A, Krishnamurthy K. Complexity Analysis on Normal and Abnormal Electrogastrograms Using Tsallis Entropy. In: 3rd International Electronic and Flipped Conference on Entropy and Its Applications 2016. Multidisciplinary Digital Publishing Institute.
• [23] Principe JC. Information theoretic learning: Rényi's entropy and kernel perspectives. Springer Science & Business Media; 2010.
• [24] Kaiser JF. On a simple algorithm to calculate the 'energy' of a signal. International Conference on Acoustics, Speech, and Signal Processing. Albuquerque, NM. 1990;(vol1):381-38.
• [25] Solnik S, DeVita P, Rider P, et al. Teager–Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio. Acta Bioeng Biomech. 2008;10(2):65-68.
• [26] Lauer RT, Prosser LA. Use of the Teager-Kaiser energy operator for muscle activity detection in children. Ann Biomed Eng. 2009;37(8):1584-1593.