Abstract
Urinary incontinence is a common female disorder. Although generally not a serious condition, it negatively affects the lifestyle and daily activity of subjects. Stress urinary incontinence (SUI) is the most versatile of several incontinence types and is distinguished by physical degeneration of the continence-providing mechanism. Some surgical treatment methods exist, but the success of the surgery mainly depends upon a correct diagnosis. Diagnosis has two major steps: subjects who are suffering from true SUI must be identified, and the SUI sub-type must be determined, because each sub-type is treated with a different surgery. The first step is straightforward and uses standard identification methods. The second step, however, requires invasive, uncomfortable urodynamic studies that are difficult to apply. Many subjects try to cope with the disorder rather than seek treatment from health care providers, in part because of the invasive diagnostic methods. In this study, a diagnostic method with a success rate comparable to that of urodynamic studies is presented. This new method has some advantages over the current one. First, it is noninvasive; data are collected using Doppler ultrasound recording. Second, it requires no special tools and is easy to apply, relatively inexpensive, faster and more hygienic.
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References
Urinary incontinence - ACOG Technical Bulletin, No. 213, October 1995 (Replaces No. 100, January 1987). Int J Gynecol Obstet. 1996; 52:7586.
Dwyer, P. L., Differentiating stress urinary incontinence from urge urinary incontinence. Int J Gynaecol Obstet. 86(Suppl 1):S17–S24, 2004.
Wilson, L., Brown, J. S., Shin, G. P., Luc, K. O., and Subak, L. L., Annual direct cost of urinary incontinence. Obstet Gynecol. 98:398–406, 2001.
Hannestad, Y. S., Rortveit, G., Sandvik, H., and Hunskaar, S., A community-based epidemiological survey of female urinary incontinence: The Norwegian EPINCONT study. J Clin Epidemiol. 53:1150–1157, 2000.
Abrams, P., Andersson, K., Brubaker, L. T., Cardozo, L., Cottenden, A., and Denis, L., Evaluation and treatment of urinary incontinence, pelvic organ prolapse, and faecal incontinence. In: Abrams, P., Cardozo, L., Khoury, S., and Wein, A. (Eds.), 3 rd International Consultation on Incontinence. Health Publication Ltd, Plymouth, UK, pp. 1589–1642, 2005.
Haliloglu, B., Karateke, A., Coksuer, H., Peker, H., and Cam, C., The role of urethral hypermobility and intrinsic sphincteric deficiency on the outcome of transobturator tape procedure: a prospective study with 2-year follow-up. Int Urogynecol J Pelvic Floor Dysfunct. 21(2):173–8, 2010.
Samuelsson, E., Victor, A., and Tibblin, G., A population study of urinary incontinence and nocturia among women aged 20–59 years. Prevalence, well-being and wish for treatment. Acta Obstet Gynecol Scand. 76(1):74–80, 1997.
Pantazis, K., and Freeman, R. M., Investigation and treatment of urinary incontinence. Current Obstetrics & Gynaecology. 16(6):344–352, 2006.
Ulmsten, U., Johnson, P., and Rezapour, M., A three-year follow up of tension free vaginal tape for surgical treatment of female stress urinary incontinence. Br J Obstet Gynaecol. 106(4):345–350, 1999.
Cheater, F., and Castleden, C., Epidemiology and classification of urinary incontinence. Best Practice & Research Clinical Obstetrics & Gynaecology. 14(2):183–205, 2000.
Segev, Y., Rosen, T., Auslender, R., Dain, L., and Abramov, Y., How painful is multichannel urodynamic testing? International Urogynecology Journal. 20(8):953–955, 2009.
Luber, K. M., The definition, prevalence, and risk factors for stress urinary incontinence. Rev Urol. 6:S3–S9, 2004.
Farahat Y, Eltatawy H, Haroun H, Abo-Ramadan A, Morad S, Rasheed M. The Small Intestinal Submucosa (SIS) as a Suburethral Sling for Correction of Stress Urinary Incontinence: Preliminary Experience. UroToday Int J. June 2009; 2(3).
Truzzi, J. C., Almeida, F. M., Nunes, E. C., and Sadi, M. V., Residual Urinary Volume and Urinary Tract Infection—When are They Linked? The Journal of Urology. 180(1):182–185, 2008.
Latifoğlu, F., Polat, K., Kara, S., and Güneş, S., Medical Diagnosis of Atherosclerosis from Carotid Artery Doppler Signals Using Principles Component Analysis (PCA), k-NN Based Weighting Pre-Processing and Artificial Immune Recognition System (AIRS). Journal of Biomedical Informatics. 41(1):15–23, 2008.
Kara, S., Latifoğlu, F., and Güney, M., Determining Fractal Dimension Of Umbilical Artery Doppler Signals Using Hurst Exponent. Journal of Medical Systems. 31(6):529–536, 2007.
Uğuz, H., and Kodaz, H., Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model. Expert Systems with Applications. 38(6):7407–7414, 2011.
Hall, L. T., Maple, J. L., Agzatian, J., and Abbot, D., Sensor system for heart sound biomonitor. Microelectron J. 31:583–592, 2000.
Cvetkovic, D., Übeyli, E. D., and Cosic, I., Wavelet transform feature extraction from human PPG, ECG and EEG signal responses to ELF PEMF exposures: A pilot study. Digital Signal Processing. 18(5):861–874, 2007.
Alsberg, B. K., Woodward, A. M., and Kell, D. B., An introduction to wavelet transforms for chemometricians: A time-frequency approach. Chemometrics and Intelligent Laboratory Systems. 37(2):215–239, 1997.
Kara, S., Latifoğlu, F., İmal, E., and Güney, M., Spectral Analysis Of Umbilical Artery Doppler Signals During Gestation Via Discrete Wavelet Transform. Experimental Techniques. 33(4):52–58, 2009.
Latifoğlu, F., Kara, S., and İmal, E., Comparison of Short-Time Fourier Transform and Eigenvector MUSIC Methods Using Discrete Wavelet Transform for Diagnosis of Atherosclerosis. J Med Syst. 33(3):189–197, 2009.
Ceylan, M., Ceylan, R., Özbay, Y., and Kara, S., Application of Complex Discrete Wavelet Transform in Classification of Doppler Signals using Complex Valued Artificial Neural Network. Artificial Intelligence in Medicine. 44(1):65–76, 2008.
Kara, S., and Dirgenali, F., A System to Diagnose Atherosclerosis via Wavelet Transforms, Principal Component Analysis and Artificial Neural Networks. Expert Systems with Applications. 32(2):632–640, 2007.
Turkoglu, I., Arslan, A., and Ilkay, E., An intelligent system for diagnosis of heart valve diseases with wavelet packet neural networks. Computer in Biology and Medicine. 33(4):319–331, 2003.
Hanbay, D., An expert system based on least square support vector machines for diagnosis of the valvular heart disease. Expert Systems with Applications. 36(3):4232–4238, 2009.
Shannon, C. E., and Weaver, W., The Mathematical Theory of Communication. Urbana, University of Illinois, 1964.
Cek, E., Ozgoren, M., and Savaci, A., Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med. 40(1):90–96, 2010.
Sabeti, M., Katebi, S., and Boostani, R., Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artificial Intelligence in Medicine. 47(3):263–274, 2009.
Dirgenali, F., and Kara, S., Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals. Expert Systems with Applications. 31(3):643–651, 2006.
Vázquez, E., A travelling wave distance protection using principal component analysis. Int. J. Elect. Power Energy Syst. 25:471–479, 2003.
Zhang, Y. X., Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 73:68–75, 2007.
Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med Biol Eng Comput. 30:449–464, 1992.
Baxt, W. G., Application of artificial neural networks to clinical medicine. Lancet. 346:1135–1138, 1995.
Edenbrandt, L., Heden, B., and Pahlm, O., Neural networks for analysis of ECG complexes. J. Electrocardiol. 26:74, 1993.
Magosso, E., Ursino, M., Zaniboni, A., and Gardella, E., A wavelet-based energetic approach for the analysis of biomedical signals: Application to the electroencephalogram and electro-oculogram. Applied Mathematics and Computation. 207(1):42–62, 2009.
Wang, X., and Paliwal, K. K., Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognition. 36(10):2429–2439, 2003.
Tagluk, M. E., Akin, M., and Sezgin, N., Classification of sleep apnea by using wavelet transform and artificial neural networks. Expert Syst. Appl. 37(2):1600–1607, 2010.
Chaudhuri, B. B., and Bhattacharya, U., Efficient Training and Improved Performance of Multilayer Perceptron in Pattern Classification. Neurocomputing. 34:11–27, 2000.
Basheer, I. A., and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods. 43:3–31, 2000.
Kara, S., and Okandan, M., Atrial Fibrillation Classification with Artificial Neural Networks. Pattern Recognition. 40(11):2967–2973, 2007.
Levenberg, K., A Method for the Solution of Certain Non-Linear Problems in Least Squares. The Quarterly of Applied Mathematics. 2:164–168, 1944.
Marquardt, D., An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math. 11:431–441, 1963.
Koker, R., Altinkok, N., and Demir, A., Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms. Materials and Design. 28:616–627, 2007.
Cattell, R. B., The scree test for the number of factors. Multivariate Behavioral Research. 1:245–276, 1966.
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This work is supported by the Scientific Research Fund of Fatih University under the project number P50060901-2.
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Tufan, K., Kara, S., Latifoğlu, F. et al. Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis. J Med Syst 36, 2159–2169 (2012). https://doi.org/10.1007/s10916-011-9680-7
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DOI: https://doi.org/10.1007/s10916-011-9680-7