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Highlighting the Current Issues with Pride Suggestions for Improving the Performance of Real Time Cardiac Health Monitoring

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Information Technology in Bio- and Medical Informatics, ITBAM 2010 (ITBAM 2010)

Abstract

Electrocardiogram (ECG) signal utilized by Clinicians to extract very useful information about the functional status of the heart. Of particular interest systems designed for monitoring people outdoor and detecting abnormalities on the real time. However, there are far from achieving the ideal of being able to perform adequately real time remote cardiac health monitoring in practical life. That is due to problematical challenges. In this paper we discuss all these issues, furthermore our intimations and propositions to relief such concerns are stated.

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References

  1. Dale Dubin, M.D.: Rapid interpretation of EKG’s, 6th edn. Cover publishing co. (2000)

    Google Scholar 

  2. Rajendra, U., Sankaranarayanan, M., Nayak, J., Xiang, C., Tamura, T.: Automatic Identification of cardiac health using modeling techniques: a comparative study. Inf. Science. 178, 457–4582 (2008)

    Google Scholar 

  3. Rodrigues, J., Goni, A., Illarramendi, A.: Real time classification of ECG on a PDA. IEEE Trans. On IT in B.med., 23–33 (2005)

    Google Scholar 

  4. Goldberger, A.L.: Electrocardiography: A Simplified Approach. Elsevier, Amsterdam (2006)

    Google Scholar 

  5. Kligfield, P.: value and limitation of 12-lead ECG monitoring. clinical window, Datex, Ohmeda (2001)

    Google Scholar 

  6. Mark, R., Moody, G.: MIT-BIH Arrhythmia data base directory. Massachusetts Institute of Technology, Cambridge (1988)

    Google Scholar 

  7. Clifford, G., Azuaje, F., McSharrg, P.: Advanced methods and tools for ECG data analysis. Artech house (2006)

    Google Scholar 

  8. Chritove, I., Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., Egiazarian, K.: Comparative study of morphological and time frequancy ECG descriptors for heartbeat classification. Medical engineering and physics 28, 876–887 (2006)

    Article  Google Scholar 

  9. Sörnmo, L., Laguna, P.: Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier, Amsterdam (2005)

    Google Scholar 

  10. de Bie, J.: P-wave trending: A valuable tool for documenting supraventricular arrhythmias and AV conduction disturbances. IEEE, 511–514 (1991)

    Google Scholar 

  11. Millet, J., Pkrez, M., Joseph, G., Mocholi, A., Chorro, J.: Previous identification of QRS Onset and Offset is not essential for classifying QRS complex in a single lead. Com. In: Cardiology, vol. 24, pp. 299–302 (1997)

    Google Scholar 

  12. Moody, G., Mark, R.: QRS Morphology Representation and Noise Estimation using the Karhunen-Loève Transform. IEEE, Comp. in Card, 269–272 (1989)

    Google Scholar 

  13. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L.: Clustering ECG complex using Hermite Functions and selforganizing maps. Trans. on B. med. Eng. 47, 838–848 (2000)

    Google Scholar 

  14. Senhadii, L., Carrault, G., Bellanger, J., Passariello, G.: Comparing wavelet transforms for recognizing cardiac patterns. IEEE, Eng. Med. & Bio., 167–173 (1995)

    Google Scholar 

  15. Herrero, G., Gotchev, A., Christov, I., Egiazarian, K.: Heartbeat classification using independent component analysis and matching Pursuits. In: ICASSP, pp. 725–728. IEEE, Los Alamitos (2005)

    Google Scholar 

  16. Christov, I., Bortolan, G.: Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Phys. Measure 25, 1281–1290 (2004)

    Article  Google Scholar 

  17. Chazal, P., Dwyer, M., Reilly, R.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51, 1196–1206 (2004)

    Article  Google Scholar 

  18. Jekova, I., Bortolan, G., Christov, I.: Assessment and comparison of different methods for heartbeat classification. Med. Eng. Phys. 30, 248–257 (2008)

    Article  Google Scholar 

  19. Osowski, S., Linh, T.: ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48, 1265–1271 (2001)

    Article  Google Scholar 

  20. Palreddy, H., Tompkins, W.: A patient-adaptable ECG beat classifier using a mixture of experts approach. Trans. on B. med. Eng. 44, 891–900 (1997)

    Article  Google Scholar 

  21. Bortolan, G., Jekova, I., Christov, I.: Comparison of four methods for premature ventricular contractions and normal beats clustering. Comp. Card. 30, 921–924 (2005)

    Google Scholar 

  22. Bashir, M.E.A., Akasha, M., Lee, D.G., Yi, M., Ryu, K.H., Bae, E.J., Cho, M., Yoo, C.: Nested Ensemble Technique for Excellence Real Time Cardiac Health Monitoring. BioComp., lasvegas, USA (2010)

    Google Scholar 

  23. Bemaid, A., Bouhouch, N., Bouhouch, R., Fellat, R., Amri, R.: Classification of ECG Patterns Using Fuzzy Rules Derived from ID3-Induced Decision Trees. In: NAFIPS, pp. 34–38. IEEE, Los Alamitos (1998)

    Google Scholar 

  24. Kampouraki, A., Manis, G., Nikou, C.: Heartbeat time series classification with support vector machines. Eng. in Med. and Bio. Sc. 13, 512–518 (2009)

    Google Scholar 

  25. Yang, T., Devine, B., Macfarlane, P.: Artificial neural networks for the diagnosis of atrial fibrillation. Med. Biol. Eng. Comp. 32, 615–619 (1994)

    Article  Google Scholar 

  26. Birman, K.: Rule-Based Learning for More Accurate ECG Analysis. Tran. on Puttern analysis and Mach. Int. 4, 369–380 (1982)

    Article  Google Scholar 

  27. Rajendra, U., Subbann, P., Iyengar, S., Raod, A., Dua, S.: Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognition 36, 61–68 (2003)

    Article  MATH  Google Scholar 

  28. Lee, D., Shon, Ho Ryu, K., Cho, M., Bae, J.: Clinica Database Based on Various Factors of Cardiovascular Diseases. In: International workshop on aware computing, Japan, pp. 604–609 (2009)

    Google Scholar 

  29. Ho Ryu, K., Kim, W., Lee, H.: A Data mining approach and framework of intelligent Diagnosis system for Coronary Artery disease Predication. In: KSES, Japan, pp. 33–34 (2008)

    Google Scholar 

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Bashir, M.E.A. et al. (2010). Highlighting the Current Issues with Pride Suggestions for Improving the Performance of Real Time Cardiac Health Monitoring. In: Khuri, S., Lhotská, L., Pisanti, N. (eds) Information Technology in Bio- and Medical Informatics, ITBAM 2010. ITBAM 2010. Lecture Notes in Computer Science, vol 6266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15020-3_21

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  • DOI: https://doi.org/10.1007/978-3-642-15020-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15019-7

  • Online ISBN: 978-3-642-15020-3

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