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Published in: Journal of Medical Systems 4/2012

01-08-2012 | ORIGINAL PAPER

Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis

Authors: Kadir Tufan, Sadık Kara, Fatma Latifoğlu, Sinem Aydın, Adem Kırış, Ünsal Özkuvancı

Published in: Journal of Medical Systems | Issue 4/2012

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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.
Literature
1.
go back to reference Urinary incontinence - ACOG Technical Bulletin, No. 213, October 1995 (Replaces No. 100, January 1987). Int J Gynecol Obstet. 1996; 52:7586. Urinary incontinence - ACOG Technical Bulletin, No. 213, October 1995 (Replaces No. 100, January 1987). Int J Gynecol Obstet. 1996; 52:7586.
2.
go back to reference Dwyer, P. L., Differentiating stress urinary incontinence from urge urinary incontinence. Int J Gynaecol Obstet. 86(Suppl 1):S17–S24, 2004.MathSciNetCrossRef Dwyer, P. L., Differentiating stress urinary incontinence from urge urinary incontinence. Int J Gynaecol Obstet. 86(Suppl 1):S17–S24, 2004.MathSciNetCrossRef
3.
go back to reference 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.CrossRef 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.CrossRef
4.
go back to reference 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.CrossRef 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.CrossRef
5.
go back to reference 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. 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.
6.
go back to reference 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.CrossRef 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.CrossRef
7.
go back to reference 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.CrossRef 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.CrossRef
8.
go back to reference Pantazis, K., and Freeman, R. M., Investigation and treatment of urinary incontinence. Current Obstetrics & Gynaecology. 16(6):344–352, 2006.CrossRef Pantazis, K., and Freeman, R. M., Investigation and treatment of urinary incontinence. Current Obstetrics & Gynaecology. 16(6):344–352, 2006.CrossRef
9.
go back to reference 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.CrossRef 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.CrossRef
10.
go back to reference Cheater, F., and Castleden, C., Epidemiology and classification of urinary incontinence. Best Practice & Research Clinical Obstetrics & Gynaecology. 14(2):183–205, 2000.CrossRef Cheater, F., and Castleden, C., Epidemiology and classification of urinary incontinence. Best Practice & Research Clinical Obstetrics & Gynaecology. 14(2):183–205, 2000.CrossRef
11.
go back to reference 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.CrossRef 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.CrossRef
12.
go back to reference Luber, K. M., The definition, prevalence, and risk factors for stress urinary incontinence. Rev Urol. 6:S3–S9, 2004. Luber, K. M., The definition, prevalence, and risk factors for stress urinary incontinence. Rev Urol. 6:S3–S9, 2004.
13.
go back to reference 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). 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).
14.
go back to reference 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.CrossRef 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.CrossRef
15.
go back to reference 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.CrossRef 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.CrossRef
16.
go back to reference 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.CrossRef 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.CrossRef
17.
go back to reference 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.CrossRef 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.CrossRef
18.
go back to reference Hall, L. T., Maple, J. L., Agzatian, J., and Abbot, D., Sensor system for heart sound biomonitor. Microelectron J. 31:583–592, 2000.CrossRef Hall, L. T., Maple, J. L., Agzatian, J., and Abbot, D., Sensor system for heart sound biomonitor. Microelectron J. 31:583–592, 2000.CrossRef
19.
go back to reference 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.CrossRef 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.CrossRef
20.
go back to reference 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.CrossRef 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.CrossRef
21.
go back to reference 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.CrossRef 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.CrossRef
22.
go back to reference 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.CrossRef 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.CrossRef
23.
go back to reference 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.CrossRef 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.CrossRef
24.
go back to reference 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.CrossRef 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.CrossRef
25.
go back to reference 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.CrossRef 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.CrossRef
26.
go back to reference 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.CrossRef 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.CrossRef
27.
go back to reference Shannon, C. E., and Weaver, W., The Mathematical Theory of Communication. Urbana, University of Illinois, 1964. Shannon, C. E., and Weaver, W., The Mathematical Theory of Communication. Urbana, University of Illinois, 1964.
28.
go back to reference Cek, E., Ozgoren, M., and Savaci, A., Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med. 40(1):90–96, 2010.CrossRef Cek, E., Ozgoren, M., and Savaci, A., Continuous time wavelet entropy of auditory evoked potentials. Comput Biol Med. 40(1):90–96, 2010.CrossRef
29.
go back to reference 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.CrossRef 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.CrossRef
30.
go back to reference 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.CrossRef 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.CrossRef
31.
go back to reference Vázquez, E., A travelling wave distance protection using principal component analysis. Int. J. Elect. Power Energy Syst. 25:471–479, 2003.CrossRef Vázquez, E., A travelling wave distance protection using principal component analysis. Int. J. Elect. Power Energy Syst. 25:471–479, 2003.CrossRef
32.
go back to reference Zhang, Y. X., Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 73:68–75, 2007.CrossRef Zhang, Y. X., Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 73:68–75, 2007.CrossRef
33.
go back to reference 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.CrossRef 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.CrossRef
34.
go back to reference Baxt, W. G., Application of artificial neural networks to clinical medicine. Lancet. 346:1135–1138, 1995.CrossRef Baxt, W. G., Application of artificial neural networks to clinical medicine. Lancet. 346:1135–1138, 1995.CrossRef
35.
go back to reference Edenbrandt, L., Heden, B., and Pahlm, O., Neural networks for analysis of ECG complexes. J. Electrocardiol. 26:74, 1993.CrossRef Edenbrandt, L., Heden, B., and Pahlm, O., Neural networks for analysis of ECG complexes. J. Electrocardiol. 26:74, 1993.CrossRef
36.
go back to reference 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.MathSciNetMATHCrossRef 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.MathSciNetMATHCrossRef
37.
go back to reference 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.MATHCrossRef 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.MATHCrossRef
38.
go back to reference 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.CrossRef 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.CrossRef
39.
go back to reference Chaudhuri, B. B., and Bhattacharya, U., Efficient Training and Improved Performance of Multilayer Perceptron in Pattern Classification. Neurocomputing. 34:11–27, 2000.MATHCrossRef Chaudhuri, B. B., and Bhattacharya, U., Efficient Training and Improved Performance of Multilayer Perceptron in Pattern Classification. Neurocomputing. 34:11–27, 2000.MATHCrossRef
40.
go back to reference Basheer, I. A., and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods. 43:3–31, 2000.CrossRef Basheer, I. A., and Hajmeer, M., Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods. 43:3–31, 2000.CrossRef
41.
go back to reference Kara, S., and Okandan, M., Atrial Fibrillation Classification with Artificial Neural Networks. Pattern Recognition. 40(11):2967–2973, 2007.MATHCrossRef Kara, S., and Okandan, M., Atrial Fibrillation Classification with Artificial Neural Networks. Pattern Recognition. 40(11):2967–2973, 2007.MATHCrossRef
42.
go back to reference Levenberg, K., A Method for the Solution of Certain Non-Linear Problems in Least Squares. The Quarterly of Applied Mathematics. 2:164–168, 1944.MathSciNetMATH Levenberg, K., A Method for the Solution of Certain Non-Linear Problems in Least Squares. The Quarterly of Applied Mathematics. 2:164–168, 1944.MathSciNetMATH
43.
44.
go back to reference 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.CrossRef 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.CrossRef
45.
go back to reference Cattell, R. B., The scree test for the number of factors. Multivariate Behavioral Research. 1:245–276, 1966.CrossRef Cattell, R. B., The scree test for the number of factors. Multivariate Behavioral Research. 1:245–276, 1966.CrossRef
Metadata
Title
Non-Invasive Diagnosis of Stress Urinary Incontinence Sub Types Using Wavelet Analysis, Shannon Entropy and Principal Component Analysis
Authors
Kadir Tufan
Sadık Kara
Fatma Latifoğlu
Sinem Aydın
Adem Kırış
Ünsal Özkuvancı
Publication date
01-08-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9680-7

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