A new approach to urinary system dynamics problems: Evaluation and classification of uroflowmeter signals using artificial neural networks

  • Authors:

  • Semih Altunay;Ziya Telatar;Osman Erogul;Emin Aydur

  • Affiliations:

  • EAS Electronic Advanced Systems Inc., Konya Yolu 35.km Golbasi, Ankara, Turkey;Ankara University Electronics Engineering Department, 06500 Tandoğan, Ankara, Turkey;Gulhane Military Medical Academy, Biomedical Engineering Center, 06018 Etlik, Ankara, Turkey;Gulhane Military Medical Academy, Department of Urology, 06018 Etlik, Ankara, Turkey

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change in urine volume. The main purpose of this study is to analyze the uroflowmetric data and to assist physicians for their diagnosis. An expert pre-diagnosis system is implemented for automatically evaluating possible symptoms from the uroflow signals. The system uses artificial neural networks (ANN) and produces a pre-diagnostic result. The outputs of ANN are classified into three groups, which are, ''healthy'', ''possible pathologic'' and ''pathologic''. The ANN is trained using back-propagation method and the inputs of the ANN are the extracted features, which are selected according to the suggestions of urology specialists. The proposed system is trained and validated using a dataset of patients, who have already diagnosed by the specialists.