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

摘要

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.