282
IETE TECHNICAL REVIEW, Vol 23, No 5, 2006

 

4. CONCLUSION

A wireless communication based portable telecardiology system for rural health care is prposed in this paper. For this purpose a wireless communication system in VHF band is used for both way transmission of ECG signal. Different important time plane features of ECG signals are also been extracted after detection of QRS, P and T waves. These time plane features are then further analyzed by an inbuilt knowledgebase and will generate an automated report that will suggest the primary treatment and preventive measures should be taken by the patient.

Mainly rural people will be benefited with this developed system. For a population of over a billion, there are only about six states of India having super specialty hospitals and that too catering to a niche urban populace. When you consider these facts coupled with the knowledge that there is no proper State health care policy, the magnitude of the inadequate medical facilities for the vast majority of the people living in rural areas can be understood. Specially, lack or absence of doctors is a common problem in all health centers or hospitals in those rural areas. The main utility of the proposed system is there is no need of presence of
doctor for consultation since the primary report will be generated by the developed knowledgebase. The system is also very much handy and cost effective hence can be implemented at all remote places.

For future development of the existing system, Natural Language Processing (NLP) techniques can be used for conversion of different medical terms of the generated report into common people’s understandable language. e.g, Hindi or Bengali for helping rural people to understand their health’s actual condition and to take the preventive measures properly, and we are carrying out to incorporate this facility in the developed system presently.


5. REFERENCES

  1. . M Rezazadeh & N Evans, Multichannel physiological monitor plus simultaneous full-duplex speech channel using a dial-up telephone line, IEEE Trans Biomed Eng, vol 37, pp 428-432, Apr 1990.

  2. . U Patel & C Babbs, A computer based, automated telephonic system to monitor patient progress in home setting, J Med Syst, vol 16, pp 101-112, 1992.

  3. M Gott, Telematics for Health, The Role of Telemedicine in Homes and Communities, Oxford, UK : Radcliffe Med Press, 1995.
 
  1. G Coyle, L Boydell & L Brown, Home telecare for the elderly, J Telemed Telecare, vol 1, pp 183-185, 1995.

  2. B Woodward & C I Richards, Design of a Telemedicine System Using a Mobile Telephone, IEEE Transactions on Information Technology in Biomedicine, vol 5, no 1, pp 13-15, March 2001.

  3. R Gero von Wagner, Sascha Schubert, Landry F Ngambia, Christian Morgenstern & Armin Bolz, Concept for an Event-triggered Electrocardiographic Telemetry- System using GSM for supervision of cardiac patients, Proceedings on Information Systems for Enhanced Public Safety and Security (EUR-Comm 2000), pp 374-377.

  4. M J Goldman, Principles of Electrocardiography, 11th Edn., Marugen Asia (Pvt.) Ltd.

  5. J R Hampton, The ECG Made Easy, 5th edn., Churchill Livingstone.

  6. Ary L Goldberger, Clinical Electrocardiography, A Simlified Approach, 6th Edn, Harcourt India Pvt Ltd.

  7. F B Hildebrand, Introduction To Numerical Analysis, T M H edn., Tata Mcgraw-Hill Publishing Company Ltd, pp 82-84.

  8. N Maglaveras, T Stamkopoulos, K Diamantaras, C Pappas & M Strintzis, ECG pattern recognition and classification using non-linear transformations and neural networks: a review, International Journal of
    Medical Informatics, vol 82, 1998, pp 191-208.

  9. http://www.infochangeindia.org/archives1.jsp?
    secno=9&monthname=December&
    year=2002&detail=T


  10. . M Lagerholm & C Peterson, Clustering ECG Complexes Using Hermite Functions and Self Organising, Maps: IEEE Trans Biomed Eng, 47 (7), 2000, 838-848.

  11. G Bortolan, C Brohet & S Fusaro, Possibilities of using neural networks for ECG classification, J Electrocardiol, 29, 1996, 10-16.

  12. R Degani, Computerized Electrocardiogram Diagnosis: Fuzzy Approach Methods of Inform
    Med, 31, 1992, 225-233.

  13. . P Bozzola et al, Ahybrid neuro-fuzzy system for ECG classification of myocardial infarction, Comput Cardiol., Indianapolis, IN, 1996.

  14. L Polkowski, A Skowron, Rough Sets in Knowledge Discovery, Physica – Verlag, Wurzburg, Wein, 1998.

  15. fhrm, Discernibility & Rough Sets in Medicine : Tools and Applications, PhD Thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, NTNU Report, 1999: 133, IDI Report 1999.