Frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks.

  • Mohsen Kazemi Department of Biotechnology and Medical Engineering, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Malaysia
  • Malarvili Bala Krishnan Department of Biotechnology and Medical Engineering, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Malaysia
  • Teo Aik Howe Emergency Department, Hospital Pulau Pinang, Pinang, Malaysia
Keywords: Asthma, Autoregressive (AR) modeling, Capnogram, Fourier analysis, Radial Basis function (RBF) network

Abstract

In this paper, the method of differentiating asthmatic and non-asthmatic patients using the  frequency analysis of  capnogram  signals is presented.  Previously, manual study on capnogram signal has been conducted  by several researchers. All past researches showed significant correlation between capnogram signals and asthmatic patients. However all of them are just manual study conducted through the conventional time domain method. In this study, the power spectral density (PSD) of capnogram signals is estimated by using Fast Fourier Transform (FFT) and Autoregressive (AR) modelling.
The  results show the  non-asthmatic  capnograms have one  component  in their  PSD estimation, in contrast to asthmatic capnograms that have two components. Furthermore, there is a significant difference between the magnitude of the first component  for both asthmatic and non-asthmatic capnograms.
 The  effectiveness and  performance  of  manipulating the  characteristics of  the  first frequency  component,  mainly its  magnitude  and  bandwidth,  to  differentiate  between asthmatic and non-asthmatic conditions by means of receiver operating characteristic (ROC) curve analysis and radial basis function (RBF) neural network were shown.
The output of this network is an integer prognostic index from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 95.65% and an error rate of 4.34%. This developed algorithm is aspired to provide a fast and low-cost diagnostic system to  help  healthcare professional involved in respiratory care as it would be  possible to monitor severity of asthma automatically and instantaneously.

References

1. Steven E. Weinberger, Barbara A. Cockrill, Jess Mandel, Principles of pulmonary medicine, 5th Ed., Elsevier Inc.,2008.
2. Rhoades C, Thomas F. Capnography: beyond the numbers. Air Med J 2002; 21(2):43-8.
3. Giner J, Casan P. Pulse Oximetry and Capnography in Lung Function Laboratories. Arch Bronconeumol 2004;40(7):311-4.
4. Thompson JE, Jaffe MB. Capnography waveforms in the mechanically ventilated patient. Respir Care 2005;50(1):100-9.
5. Smalhout B., Kalenda Z., An Atlas of Capnography, 2nd Ed., Kerckebosche Zeist Press, 1981.
6. You B, Peslin R, Duvivier C, Vu V, Grilliat JP.Expiratory capnography in asthma. Eur Respir J 1994: 7(2):318-23.
7. Yaron M, Padyk P, Hutsinpiller M, Cairns CB. Utility of the expiratory capnogram in the assessment of bronchospasm. Ann Emerg Med 1996; 28(4):403-7.
8. Druck J, Rubio PM, Valley MA, Jaffe MB, Yaron M.Evaluation of the slope of phase III from the volumetric capnogram as a non-effort dependent in acute asthma exacerbation. Annual of Emergency Medicine 2007;50(3):130-6.
9. Tan Teik Kean, M. B. Malarvili. Analysis of capnography for asthmatic patient, IEEE International Conference on Signal and Image Processing Applications 2009, 464-7.
10. Swenson J, Henao-Guerrero PN, Carpenter JW. Clinical Technique: Use of Capnography in Small Mammal Anesthesia. Journal of Exotic Pet Medicine 2008:17(3):175-80.
11. Kirkko-Jaakkola M, Collin J, Takala J. Bias Prediction for MEMS Gyroscopes, IEEE Sensors 2012; 12(6):2157-63.
12. Facchinetti A, Sparacino G, Cobelli C. An Online Self- Tunable Method to Denoise CGM Sensor Data. IEEE Trans Biomed Eng 2010: 57(3):634-41.
13. Gonzalo R. Arce, Nonlinear Signal Processing; A Statistical Approach, John Wiley & Sons, Inc., New Jersey, 2005.14. Gao S, Mateer T. Additive Fast Fourier Transforms Over Finite Fields. IEEE Transactions on Information Theory2010; 56(12):6265-72.
15. Lauralee Sherwood. Fundamentals of Physiology: A Human Perspective, Thomson Brooks and Cole Inc.,2006.
16. Soni RK, Jain A, Saxena R. An improved and simplified design of Pseudo-transmultiplexer using Blackman window family. Digital Signal Processing 2010;20(3):743-9.
17. John L. Semmlow, Biosignal and Biomedical Image Processing, Marcel Dekker Inc., 2004.
18. Alan V. Oppenhein, Ronald W. Schafer, Discrete-Time Signal Processing, Prentice Hall Signal Processing Series,3rd Ed., 2010.
19. Hsu HW, Liu CM. Autoregressive Modeling of Temporal/Spectral Envelopes With Finite-Length Discrete Trigonometric Transforms. IEEE Transactions on Signal Processing 2010; 58(7):3692-705.
20. Takalo RH, Ihalainen HH. Tutorial on Univariate Autoregressive Spectral Analysis Export. J Clin Monit Comput 2006; 20:379-88.
21. Tracey Cassar, Kenneth P. Camilleri, Simon G. Fabri, Order Estimation of Multivariate ARMA Models. IEEE journal of Selected Topics in Signal Processing 2010;4(3):494-503.
22. K J Blinowska, J Zygierewicz, Practical Biomedical Signal Analysis, Poland, CRC Press (Taylor & Francis Group, LLC), 2011.
23. Fang-Xiang Wu, Wen-Jun Zhang, Dynamic-Model-Based Method for Selecting Significantly Expressed Genes From Time-Course Expression Profiles. IEEE Trans Inform Technol Biomed 2010; 14(1):16-22.
24. Temko A, Lightbody G, Thomas EM, Boylan GB, Marnane W. Instantaneous Measure of EEG Channel Importance for Improved Patient-Adaptive Neonatal Seizure Detection. IEEE Trans Biomed Eng 2012;59(3):717-27.
25. Ramachandran P, Lu WS, Antoniou A. Filter-Based Methodology for the Location of Hot Spots in Proteins and Exons in DNA. IEEE Biomed Eng 2012; 59(6):1598-1609.
26. Acharya UR, Dua S, Du X, Sree S V, Chua CK.Automatic Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features. IEEE Trans Inf Technol Biomed 2011; 15(3):449-55.
27. M. D. Buhmann, Radial Basis Functions: Theory and Implementations, Cambridge University Press, Cambridge, 2004.
28. Tiantian Xie, Hao Yu, Joel Hewlett, Pawel Rozycki, Bogdan Wilamowski. Fast and Efficient Second-order Method for Training Radial Basis Function Networks. IEEE Transactions on Neural Networks and Learning Systems 2012; 23(4):609-19.

How to Cite
1.
Kazemi M, Bala Krishnan M, Aik Howe T. Frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks. Iran J Allergy Asthma Immunol. 12(3):236-4.
Section
Articles