Comparison of three classifiers in detection of obstruction of the lower urinary tract using recorded sounds of voiding

Abstract

The aim of this research is to help health care professionals to automatically detect lower urinary tract disorders using sounds of voiding recorded at home. In total 93 patients were diagnosed as obstructed or non-obstructed in a hospital using traditional flow-metering technique. After they went to their houses to collect several micturition recordings (5–13 records per patient) by themselves using their Oppo smart watch. Our proposed method is based on the use of the wavelet scalogram to represent the collected sounds as images, which contains both time and frequency information. A deep learning model, the inception v3 convolutional neural network, is used to classify these recordings of the voiding into the categories of obstructed and non-obstructed. We compared the performance of our approach with classical techniques such as Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using the envelope of the superposed sounds per patient as inputs. These recordings were obtained in home environments. The ground truth was built by physicians’ labeling these sound recording. They used the gold standard uroflowmetry test, which gave them all the information to classify the patients as either obstructed or non-obstructed. The performance of the model in terms of the F1 score, accuracy, and area under the curve were 0.897, 0.891 and 0.901, respectively.