Criteria and Procedures for Estimating the Informativity and Feature Selection in Biomedical Signals for their Recognition

Authors

  • A. P. Shulyak National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev
  • A. D. Shachykov National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev

DOI:

https://doi.org/10.20535/RADAP.2016.66.79-86

Keywords:

biomedical signals, recognition, features informativity, supervised learning

Abstract

Introduction. The issues of features informativity evaluation in biomedical signals portraits according to formal comparison of their probability values, obtained during training of supervised learning recognition systems are being considered. The purpose of this work is to increase the effectiveness of the biomedical signals recognition in diagnostic systems with supervised learning, by choosing rational structure of portraits based on the nature of their elements influence on the quality of pattern recognition.

Details. Informativity values are used for feature selection in the formation of truncated portraits. Subsequent exclusion of less informative features from consideration relies in this paper in the basis of disclosure and implementation of reserves to increase the probability of correct recognition in diagnostic systems. Content and efficiency of the proposed signal processing technology is illustrated by the test case in the application to the common task of recognition of QRS-complexes types, useful for recognition system training with its data selection.

Conclusions. The use of certain components of biomedical signals portraits, studied in the diagnosis of patients can have a positive impact on the quality of pattern recognition, while others create redundancy of portraits or reducing effectiveness of solving this task.

Author Biographies

A. P. Shulyak, National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev

Shulyak A. P.

A. D. Shachykov, National Technical University of Ukraine, Kyiv Politechnic Institute, Kiev

Shachykov A. D.

References

Soni J., Ansari U., Sharma D. and S. Soni (2011) Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications, Vol. 17, No. 8, pp. 43-48. DOI: 10.5120/2237-2860

Qeethara Kadhim Al-Shayea (2011) Artificial neural network in medical diagnosis. IJCSI International Journal of Computer Science, Vol. 8, Iss. 2, pp. 150-154.

Genkin A. A. (1999) New information technology of the analysis of medical data. OMIS program complex. St. Petersburg, Politekhnika Publ., 191 p. (in Russian)

Antomonov M. U. (2006) Mathematical processing and analysis of biomedical data, 558 p. (in Ukrainian).

Vasil'ev V. I. (1983) Recognition systems. Kiev, Naukova dumka ( in Russian).

Li M. and Zhou Z. H. (2007) Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 37, No. 6, pp. 1088-1098. DOI : 10.1109/tsmca.2007.904745

Shulyak A. and Shachykov A. (2015) Development of principles for analyzing the structure of cyclic biomedical signals for their detection, recognition and classification. Visnyk NTUU “KPI”. Seriia Pryladobuduvannia, No 49, pp. 169-179.

Pechenizkiy M., Tsymbal A., Puuronen S. and Pechenizkiy O. (2006) Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction. 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), pp. 708-713. DOI: 10.1109/cbms.2006.65

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, No. 101(23), e215-e220. Available at: http://circ.ahajournals.org/cgi/content/full/101/23/e215

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Published

2016-09-30

How to Cite

Shulyak, A. P. and Shachykov, A. D. (2016) “Criteria and Procedures for Estimating the Informativity and Feature Selection in Biomedical Signals for their Recognition”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, 0(66), pp. 79-86. doi: 10.20535/RADAP.2016.66.79-86.

Issue

No. 66 (2016)

Section

Radioelectronics Medical Technologies

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