Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats

Tantin, A.; Assi, Elie Bou; Asselt, Els van; Hached, Sami

doi:10.1016/j.bspc.2019.101667

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…A major advantage of the proposed method is that it is free from defining a threshold and does not require pre-training as in the previous methods [23][24][25][26][27][28][29][30][31][32][33][34][35] . Inter-subject and intra-subject variability reduce the generalization ability of the threshold-based methods seriously, which further limited the applications of methods in real life.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, a method based on machine learning was proposed for the prediction of voiding contraction using the band-power of the measured bladder pressure in anesthetized rats 35 . A linear discriminant analysis (LDA) classifier using the band-power of the measured bladder pressure as the feature inputs was able to predict voiding contractions by more than 25 s in advance.…”

Section: Introductionmentioning

confidence: 99%

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Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

Qasemi,

Aminian,

Erfanian

2023

Preprint

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The use of electrical stimulation devices to manage bladder incontinence relies on the application of continuous inhibitory stimulation. However, continuous stimulation can result in tissue fatigue and increased delivered charge. Here, we employ a real-time algorithm to provide a short-time prediction of urine leakage using the high-resolution power spectrum of the bladder pressure during the presence of non-voiding contractions (NVC) in normal and overactive bladder (OAB) cats. The proposed method is threshold-free and does not require pre-training. The analysis revealed that there is a significant difference between voiding contraction (VC) and NVC pressures as well as band powers (0.5-5 Hz) during both normal and OAB conditions. Also, most of the first leakage points occurred after the maximum VC pressure, while all of them were observed subsequent to the maximum VC spectral power. Kalman-Fuzzy method predicted urine leakage on average 2.17 s and 1.64 s before its occurrence and an average of 1.96 s and 1.13 s after the contraction started in normal and OAB cats, respectively, with a 100% success rate. This work presents a promising approach for developing a neuroprosthesis device, with on-demand stimulation to control bladder incontinence.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

Qasemi,

Aminian,

Erfanian

2023

Preprint

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“…Tujuan dari ekstraski fitur adalah mempertahankan sebagian besar informasi yang relevan dengan melakukan kompresi. Beberapa teknik dari ekstraksi fitur adalah Principal Component Analysis (PCA) [15]- [18], Linear Discriminant Analysis (LDA) [19]- [22], dan Fast Fourier Transform (FFT) [23]- [27].…”

Section: Pendahuluanunclassified

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

Prasetiyowati

2024

INFORMAL

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There are several methods for performing feature extraction, one of which is the Fast Fourier Transform (FFT). FFT is an effective way to convert signals or time series data to the frequency domain. This study uses the FFT method for image data. Image data is transformed using FFT and returned using the Inverse Fast Fourier Transform (IFFT). From the results of the feature extraction test on nine image files, it was found that 78% of the image data experienced a decrease in size, 11% of the images had a fixed size, and 11% of the image data experienced an increase in size.

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“…Taku et al [ 7 ] used a mathematical model and linear regression analysis to predict bladder volume in an experimental setting. Tantin et al [ 22 ] used linear discriminant analysis to forecast bladder voiding. The authors used a model based on linear discriminant analysis in lab rats.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One of the disadvantages of prediction models in the literature is that the data used for building models are limited. For example, the models proposed by Tantin et al [22] did not examine human bladder voiding. Fechner et al 's [6] prediction model is based on data that only included males between 20 and 30 years old.…”

Section: Literature Reviewmentioning

confidence: 99%

Mitigating urinary incontinence condition using machine learning

Ali

Ahmed

Olivos

et al. 2022

BMC Med Inform Decis Mak

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Background Urinary incontinence (UI) is the inability to completely control the process of releasing urine. UI presents a social, medical, and mental issue with financial consequences. Objective This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI. Method A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time. The participants were instructed to record input data (such as the time of consumption and the number of drinks) and output data (i.e., the time the individual urinated). Other factors, such as age and BMI, were also considered. The study was conducted in two phases: (1) data was prepared for modeling, including missing values, data encoding, and scaling; and (2) a classification model was designed with four output classes of the next urination time: < = 30 min, 31–60 min, 61–90 min, > 90 min. The model was built in two steps: (1) feature selection and (2) model training and testing. Feature selection methods such as lasso regression, decision tree, random forest, and chi-square were used to select the best features, which were then used to train an extreme gradient boosting (XGB) algorithm model to predict the class of the next urination time. Result The feature selection steps resulted in nine features considered the most important features affecting UI. The accuracy, precision, recall, and F1 score of the XGB predictive model are 0.70, 0.73, 0.70, and 0.71, respectively. Conclusion This research is the first step in developing a machine learning model to predict when a person will need to urinate. A precise predictive instrument can enable healthcare providers and caregivers to assist people with various forms of UI in reliable, prompted voiding. The insights from this predictive model can allow future apps to go beyond current UI-related apps by predicting the time of urination using the most relevant factors that impact voiding frequency.

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Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats

Cited by 10 publications

References 21 publications

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

Real-time prediction of bladder urine leakage using fuzzy inference system and dual Kalman filtering in cats

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

Mitigating urinary incontinence condition using machine learning

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