Classification of Uroflowmetry and EMG Signals of Pediatric Patients using Artificial Neural Networks

Abstract

UF-EMG test, in which non-invasive uroflowmetry (UF) and electromyography (EMG) signals are simultaneously recorded, is frequently used in children diagnosed with lower urinary tract dysfunction disease (AUSD) and its treatment. In the literature, independent (single) UF signals and integrated (dual) UF-EMG signals are graded many times but there is no classification study of UF-EMG integrated signals with Artificial Neural Networks (ANN), although studies have been done to classify UF signals with ANN. In this paper, it was aimed to classify the UF-EMG signals recorded from pediatric patients during the UF-EMG tests in Urodinami Center of Gulhane Education and Research Hospital using ANN. 773 (80%) of the 967 patients with an average age of 8 were used for training and 194 (20%) were used for the test. In YSA, the contribution of the features obtained from the EMG signals played a crucial role and was the main reason to improve the signal classification from 58% to 84.02%. The new classification method created by the obtained data does facilitate the interpretation of UF-EMG results for the clinical personnel in diagnosis, follow-up and treatment of patients. It is also aimed that the pediatric patients living in regions with less access to health care can be treated by providing an early and easy preliminary diagnostic tool.