@article {Jin:2020:0736-2935:5807,
title = "Prediction of urinal flowrate by using urinary acoustic signals based on LSTM neural network",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2020",
volume = "261",
number = "1",
publication date ="2020-10-12T00:00:00",
pages = "5807-5810",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2020/00000261/00000001/art00093",
author = "Jin, Jie and Chung, YoungBeen and Kim, WanSeung and Jeon, JinYong and Ho, JeongGyu and Jo, JungKi and Park, JunHong",
abstract = "To measure urinary patterns of the patients with urination disorders anywhere and to predict various symptoms of lower urinary tract ultimately, this study was conducted to produce a device that was capable of analysing and classifying the urinary acoustic signals. Assuming the loudness
representing the magnitude of the urinary sound, the roughness representing the signal change pattern as inputs and the flowrate measured through the uroflowmetry test as output, the LSTM model for time series prediction was trained and the system was constructed. The predicted values using
LSTM model and the measured result are quite similar. In addition, based on the predicted time series velocity results, it is possible to check the health status of the urinary system through the LSTM classification model and the abnormal symptoms of the lower urinary tract can be suspected.
Therefore, it is shown that the LSTM prediction and classification model proposed in this study can be applied to the clinic for predicting various symptoms with urinary acoustic signals.",
}