New Intelligent-based Approach for the Early Detection of Disorders: Use on Rhinological Data

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Author(s)

Alina S. Nechyporenko 1,*

1. Kharkiv National University of Radioelectronics, Biomedical Engineering Department,Software Engineering Department Kharkiv, 61166, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2017.08.01

Received: 29 May 2017 / Revised: 10 Jun. 2017 / Accepted: 18 Jun. 2017 / Published: 8 Aug. 2017

Index Terms

Time series, early detection of disorders, classification algorithms, rhinomanometric signals

Abstract

Medical data are characterized by complexity, inaccuracy, heterogeneity, the presence of hidden dependencies, often their distributions are unknown. Correlations between factors of disorders, including clinical data, parameters of time series, patient’s subjective assessments have a high complexity that cannot be fully comprehended by humans anymore. This problem is extremely important especially in case of the early detection of disorders. Machine learning methods are very useful for such detection task. Special area of interest is a problem of breathing disorders. In the paper, author demonstrates the potential use of computational intelligence tools for rhinologic data processing. Implementation of supervised learning techniques will allow improving accuracy of disorders detection as well as decrease medical insurance company expenses. Proposed intelligent-based approach makes it possible to process a variety of heterogeneous data in the medical domain. A combination of conventional and fractal features for time series of rhinomanometric data as well as inclusion of hydrodynamic characteristics of nasal breathing process provides the best accuracy. Such approach may be modified for other breathing disorders detection.

Cite This Paper

Alina S. Nechyporenko,"New Intelligent-based Approach for the Early Detection of Disorders: Use on Rhinological Data", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.8, pp.1-8, 2017. DOI: 10.5815/ijigsp.2017.08.01

Reference

[1]A. A. Morsy, K. M. Al-Ashmouny, “Sleep Apnea Detection Using an Adaptive Fuzzy Logic Based Screening System”, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, pp. 6124-27, 2005.

[2]J.S. Reynolds, W.T. Goldsmith, J. B. Day, A. A. Abaza, A.M. Mahmoud, A.A. Afshari, Jacob B. Barkley, E.L. Petsonk, M.L. Kashon, D.G. Frazer, “Classification of voluntary cough airflow patterns for prediction of abnormal spirometry”, Journal of biomedical and health informatics, Vol.20, Iss.3, pp. 963-969, 2016.

[3]A. Abusharka, M. Faezipour, “Acoustic signal classification of breathing movements to virtually aid breath regulation”, Journal of biomedical and health informatics, Vol. 17, Issue 2, pp. 493-500, 2013.

[4]C. Chaves, C. Ribeiro de Andrade, C. Ibiapina, “Objective measures for functional diagnostic of the upper airways: practical aspects”, Rhinology, Vol. 52, no. 2, pp. 99-103, 2014.

[5]K. P. Exarchos, Y. Goletsis, “Multiparametric decision support system for the prediction of oral canser reoccurrence”, Journal of biomedical and health informatics, Vol.16, Iss.6, pp. 1127-1134, 2012.

[6]Xie B., Minn H., “Real-time sleep apnea detection by classifier combination”, IEEE Transactions on information technology in biomedicine, vol. 16, no. 3, pp. 469-477, 2012.

[7]N. Bogunovic, A. Jovic, “Biomedical nonlinear signals by data mining methods”, Proceedings of 17th Conference on Systems, Signals and Image Processing IWSSIP, pp. 276-280, 2010.

[8]A. Nechyporenko, “Rhinomanometric signal processing for selection of formalized diagnostic criterion in rhinology”, Telecommunications and Radio Engineering, Vol.74, no.14, pp. 1285-1294, 2015.

[9]K. Vogt, A. A. Jalowayski, W. Althaus, C. Cao, D. Han, W. Hasse, H. Hoffrichter, R. Mosges, J. Pallanch, K. Shah-Hosseini, K. Peksis, K. D. Wernecke, L. Zhang and P. Zaporoshenko, “4-Phase- Rhinomanometry (4PR) – basics and practice 2010”, Rhinology Suppl. 21, pp. 1-50, 2010.

[10]H. L. Thulesius, “Rhinomanometry in clinical use. A tool in the septoplasty decision making process”, Ph.D. dissertation, Department of Otorhinolaryngology, Head and Neck Surgery, Clinical Sciences, Lund University Sweden, 2012.

[11]P. Wheeler, S. Wheeler, “Vasomotor rhinitis,”Am. Fam. Physician, no. 72(6), pp. 1057–62, 2005.

[12]V.V. Chmovzh, A.S. Nechyporenko, O.G. Garyuk, “System approach to finding hydrodynamic resistance coefficient on a nasal cavity”, Computer science, information technology, automation journal, № 4, pp. 8-15, 2016.

[13]F. Chometon, P. Gillieron, J. Laurent et al., “Aerodynamics of nasal airways with application to obstruction,” Proceedings of the 6th Triennial International Symposium on Fluid Control, Measurement and Visualization, pp. 65–71, 2000.

[14]C. Chaves, C. Ribeiro de Andrade, C. Ibiapina, “Objective measures for functional diagnostic of the upper airways: practical aspects”, Rhinology, Vol. 52, no 2, pp. 99-103, 2014.

[15]A. Yerokhin, A. Nechyporenko, A. Babii, O. Turuta, “Usage of F-transform to Finding Informative Parameters of Rhinomanometric Signals”, Proc. of the X International Scientific and Technical Conference “Computer Science and Information Technologies CSIT 2015”, Lviv, 14-17 September, pp. 129-132, 2015.

[16]S. L. Marpl, Digital spectral analysis with applications, Prentice Hall, New-Jersey, 571 p, 1990.

[17]J. Spilka, V.Chudacek, M.Koucky, L.Lhotska, M.Huptych, P.Janku, G. Georgoulas, C. Stylios, “Using nonlinear features for fetal heart rate classification”, Biomedical signal processing and control, № 7, p. 350-357, 2012.

[18]O. V. Spirintseva, “The Multifractal Analysis Approach for Photogrammetric Image Edge Detection”, Journal of Image, Graphics and Signal Processing, 12, pp. 1-7, 2016.

[19]E. B. Kern, “Committee report on standardization of rhinomanometry,” Rhinology, vol. 19(4), pp. 231-236, 1981.

[20]M. Gasparovica-Asite, I. Polaka, L. Alekseyeva, “The impact of feature selection on the information held in bioinformatics data”, Information Technology and Management Science, 18, pp. 115-121, 2015.

[21]A. L. Yerokhin, A.S. Babii, A.S. Nechyporenko, O.P. Turuta, “A Lars-Based Method of the Construction of a Fuzzy Regression Model for the Selection of Significant Features”, Cybernetics and Systems Analysis, Vol.52, no.4, pp 641–646, 2016.

[22]C. Cortes and V. Vapnik, “Support-vector network”, Machine Learning, Vol. 20, no. 3, pp. 273-297, 1995.

[23]L. Breiman, “Random forests”, Machine Learning, no. 45(1), pp. 5–32, 2001.

[24]N. Indurkhya, F. J. Damerau, Handbook of Natural Language Processing, Second Edition, - Chapman & Hall/CRC Machine Learning & Pattern Recognition, 2nd Edition, 704 p., 2010.

[25]S. Venkatalakshmi, J. Janet, “Classification of Mammogram Abnormalities Using Pseudo Zernike Moments and SVM”, Journal of Image, Graphics and Signal Processing, 4, pp. 30-36, 2017.