Microcontroller - Based System for Electrogastrography Monitoring Through Wireless Transmission

S. Haddab 1  and M. Laghrouche 1
  • 1 Department of Electronics, Mouloud MAMMERI University, Po Box 17 RP 15000, Tizi Ouzou, Algeria

Microcontroller - Based System for Electrogastrography Monitoring Through Wireless Transmission

Electrogastrography (EGG) is a non-invasive method for recording the electrical activity of the stomach. This paper presents a system designed for monitoring the EGG physiological variables of a patient outside the hospital environment. The signal acquisition is achieved by means of an ambulatory system carried by the patient and connected to him through skin electrodes. The acquired signal is transmitted via the Bluetooth to a mobile phone where the data are stored into the memory and then transferred via the GSM network to the processing and diagnostic unit in the hospital. EGG is usually contaminated by artefacts and other signals, which are sometimes difficult to remove. We have used a neural network method for motion artefacts removal and biological signal separation.

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  • Tura, A., Badanai, M., Longo, O., Quareni, L. (2003). A medical wearable device with wireless Bluetooth based data transmission. Measurement Science Review, 3, 1-4.

  • Rasid, M.F.A., Woodward, B. (2005). Bluetooth telemedicine processor for multichannel biomedical signal transmission via mobile cellular networks. IEEE Transactions on Information Technology in Biomedicine, 9 (1), 35-43.

  • Chien, J.-R.C., Tai, C.C. (2004). A wireless Bluetooth device applied to a non-contact type breathing monitoring system. In IEEE International Conference on Networking, Sensing and Control, March 21-23, 2004, Vol. 1, 172-173.

  • Chien, J.-R.C., Tai, C.C. (2006). Handheld electrocardiogram measurement instrument using a new peak quantification method algorithm built on a system-on-chip embedded system. Review of Scientific Instruments, 77 (9), 095106.

  • Woodward, B., Istepanian, R.S.H., Richards, C.I. (2001). Design of a telemedicine system using a mobile telephone. IEEE Transactions on Information Technology in Biomedicine, 5 (1), 13-15.

  • Rubel, P., Fayn, J., Nollo, G., Assanelli, D., Li, B., Restier, L., et al. (2005). Toward personal health in cardiology. Results from the EPI-MEDICS telemedicine project. Journal of Electrocardiology, 38 (4), 100-106.

  • Haddab, S., Bouchoucha, M., Cugnenc, P.-H., Barbier, J.-P. (1990). New method for electro gastrographic analysis. In Proceedings of Third Annual IEEE Symposium on Computer-Based Medical Systems, June 3-6, 1990, 418-425.

  • Alvarez, W.C. (1922). The electrogastrogram and what it shows. Journal of the American Medical Association, 78, 1116-19.

  • Lin, Z., Eaker, E.Y., Sarosiek, I., McCallum, R.W. (1999). Gastric myoelectrical activity and gastric emptying in patients with functional dyspepsia. American Journal of Gastroenterology, 94, 2384-2389.

  • Verhagen, M.A.M.T., van Schelven, L.J., Samsom, M., Smout, A.J.P.M. (1999). Pitfalls in the analysis of electrogastrographic recordings. Gastroenterology, 117, 453-460.

  • Huizinga, J.D., Robinson, T.L., Thomsen, L. (2000). The search for the origin of rhythmicity in intestinal contraction; from tissue to single cells. Neurogastroenterology and Motility, 12 (1), 3-9.

  • Hubka, P., Rosí, V., Ždiňák, J., Tyšler, M., Hulín, I. (2005). Independent component analysis of electrogastrographic signals. Measurement Science Review, 5, 21-24.

  • Simonian, H.P., Panganamamula, K., Chen, J.Z., Fisher, R.S., Parkman, H.P. (2003). Multichannel electro - gastrography (EGG) in symptomatic patients: A single center study. Neurogastroenterology and Motility, 15, 338.

  • Liang, J., Cheung, J., Chen, Z. (1997). Detection and deletion of motion artefacts in electrogastrogram using feature analysis and neural networks. Annals of Biomedical Engineering, 25, 850-857.

  • Chen, J., Lin, Z., Wu, Q., McCallum, R.W. (1995). Non-invasive identification of gastric contractions from surface electrogastrogram using back-propagation neural networks. Medica Engineering and Physics, 17, 219-225.

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