Preliminary Study of Assessing Bladder Urinary Volume Using Electrical Impedance Tomography

State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients.

Urinary Bladder Volume Monitoring Using Magnetic Induction Tomography: A Rotational Simulation Model for Anatomical Slices within the Pelvic Region

Urinary bladder volume monitoring can benefit from contactless measurements, as alternative to the traditional medical methods of transurethral catheterization or ultrasound examination. The emerging modality of Magnetic Induction Tomography (MIT) offers the possibility for estimation of the intravesical volume in the physiological and pathological states using conductivity map reconstructions of the tissues present in the pelvic region. Within MIT, eddy currents originating from the conductive urine can produce their own magnetic field in response to an external magnetic source that is susceptible of being detected outside the body by means of a static ring of sensing coils. However, the ill-conditioned and ill-posed nature of the MIT Inverse Problem make the numerical implementation and conductivity estimation highly laborious. In this paper, we present a rotational frame model based on the MIT principles with application in urodynamic studies, which allows to extend the number of contactless measurements without increasing the overall dimension of the simulation domain, at the expense of solving multiple MIT Forward Problems. On the inversion process, the single-step Gauss-Newton method with Laplacian regularizer is recruited to estimate the bladder volume non-invasively and remotely (estimation error of 19%), paving the way for this technique to surpass the current limitations found in intravesical volume monitoring in quasi-real time.

A comprehensive survey on non-invasive wearable bladder volume monitoring systems

Measuring the volume of urine in the bladder is a significant issue in patients who suffer from the lack of bladder fullness sensation or have problems with timeliness getting to the restroom, such as spinal cord injury patients and some of the elderlies. Real-time monitoring of the bladder, therefore, can be highly helpful for urinary incontinence. Bladder volume monitoring technologies can be divided into two distinct categories of invasive and non-invasive. In invasive techniques, a catheter is directly inserted into the urethra to measure the amount of urine accurately. However, it is painful, limits the user's ordinary movements, and may hurt the urinary tract. Current non-invasive techniques measure the volume of the bladder from the skin using different stationary or portable apparatus at health centers. Both techniques have difficulties and are not cost-effective to use for a long period. Recently, both invasive and non-invasive methods have been attempted to be produced in the form of wearable devices utilizing different sensing and communication technologies. Wearable bladder monitoring devices can be easily used by patients with no or few clinical steps, making them much more affordable than non-wearable devices. While wearable devices seem to be a highly convenient and effective solution, they suffer from few drawbacks, such as relatively low precision. Hence, a great number of studies have been conducted to address these issues. In this article, we review and discuss non-invasive and minimally invasive methods for monitoring the bladder volume. We focus on the most practical and state-of-the-art methods employed in wearable devices, classify them by engineering and medical characteristics, and investigate their specifications, architectures, and measurement algorithms. This study aims to introduce the latest advances in this field to practitioners while comparing the advantages and disadvantages of existing approaches. Our study concludes with open problems and future trends in the area of bladder monitoring and measurement systems. Graphical abstract Wearable bladder monitoring system.

Bladder Volume Monitoring Using Electrical Impedance Tomography With Simultaneous Multi-Tone Tissue Stimulation and DFT-Based Impedance Calculation Inside an FPGA

In this article, a novel method for measuring the volume of the urinary bladder non-invasively is presented that relies on the principles dictated by Electrical Impedance Tomography (EIT). The electronic prototype responsible for injecting innocuous electrical currents to the lower abdominal region and measuring the developed voltage levels is fully described, as well as the computational models for resolution of the so-called Forward and Inverse Problems in Imaging. The simultaneous multi-tone injection of current provided by a high performance Field Programmable Gate Array (FPGA), combined with impedance estimation by the Discrete Fourier Transform (DFT) constitutes a novelty in Urodynamics with potential to monitor continuously the intravesical volume of patients in a much faster and comfortable way than traditional transurethral catheterization methods. The resolution of the Inverse Problem is performed by the Gauss-Newton method with Laplacian regularization, allowing to obtain a sectional representation of the volume of urine encompassed by the bladder and surrounding body tissues. Experimentation has been carried out with synthetic phantoms and human subjects with results showing a good correlation between the levels of abdominal admittivity acquired by the EIT system and the volume of ingested water.

Robustness of focused and global impedance estimates of bladder volumes against uncertainty of urine conductivity

Bioimpedance measurements are currently used to monitor various biological processes and are potentially useful for studies of urodynamics. Global impedance (GI) and focused impedance measurements (FIM) can be used to monitor bladder volumes, but these are subject to varying conductivity of urine. To address this, we emulated a human bladder using an agar phantom filled with saline solutions of varying conductivities and estimated volumes using a modified FIM-based approach. Using this novel strategy, electrical potentials did not change significantly with constant liquid volumes, even when the conductivity of the saline solutions was varied between 1.027 to 1.877 and 2.610 S/m. Conversely, GI and classic FIM measurements of constant liquid volumes varied with conductivity. These observations suggest that the proposed FIM approach is suitable for bladder volume estimation due to its robustness against uncertainties of conductivity. The bioimpedance hardware used in our experiments comprised 8 electrodes and a a small and low cost impedance measurement system based on an AFE4300 direct impedance measurement device.

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance

Purpose

The aim of this study was to document more appropriate electrode location of a four-electrode-based electrical impedance technology in the monitoring of bladder filling, and to characterize the relationship between bladder filling duration and the measured electrical impedances.

Methods

A simulation study, based on a 2-dimension computational model, was conducted to determine the preferable locations of excitation and measurement electrodes in a conventional four-electrode setup. A human observation study was subsequently performed on eight healthy volunteers during natural bladder urine accumulation to validate the result of the simulation study. The correlation between the bladder filling time and the measured electrical impedance values was evaluated.

Results

The preferable location of measurement electrodes was successively validated by the model simulation study and human observation study. Result obtained via the selected electrodes location revealed a significant negative correlation (R = 0.916 ± 0.059, P < 0.001) between the measured electrical impedance and the urine accumulation time, which was consistent with the result of simulation study.

Conclusions

The findings in this study not only documented the desirable electrodes location to monitor the process of bladder urine accumulation using four-electrode measurement, but also validated the feasibility of utilizing electrical impedance technique to monitor and estimate the bladder urine volume for those with urological disorders.

Image-based classification of bladder state using electrical impedance tomography

OBJECTIVE

In this study, we examine the potential of using machine learning classification to determine the bladder state ('not full', 'full') with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner.

APPROACH

Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features.

MAIN RESULTS

The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error.

SIGNIFICANCE

We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.

Evaluation of electrical impedance tomography for determination of urinary bladder volume: comparison with standard ultrasound methods in healthy volunteers

Background

Continuous non-invasive urinary bladder volume measurement (cystovolumetry) would allow better management of urinary tract disease. Electrical impedance tomography (EIT) represents a promising method to overcome the limitations of non-continuous ultrasound measurements. The aim of this study was to compare the measurement accuracy of EIT to standard ultrasound in healthy volunteers.

Methods

For EIT of the bladder a commercial device (Goe MF II) was used with 4 different configurations of 16 standard ECG electrodes attached to the lower abdomen of healthy participants. To estimate maximum bladder capacity (BCmax) and residual urine (RU) two ultrasound methods (US-Ellipsoid and US-L × W × H) and a bedside bladder scanner (BS), were performed at the point of urgency and after voiding. For volume reference, BCmax and RU were validated by urine collection in a weight measuring pitcher. The global impedance method was used offline to estimate BCmax and RU from EIT.

Results

The mean error of US-Ellipsoid (37 ± 17%) and US-L × W × H (36 ± 15%) and EIT (32 ± 18%) showed no significant differences in the estimation of BCmax (mean 743 ± 200 ml) normalized to pitcher volumetry. BS showed significantly worse accuracy (55 ± 9%). Volumetry of RU (mean 152.1 ± 64 ml) revealed comparable higher errors for both EIT (72 ± 58%) and BS (63 ± 24%) compared to US-Ellipsoid (54 ± 25%). In case of RU, EIT accuracy is dependent on electrode configuration, as the Stripes (41 ± 25%) and Matrix (38 ± 27%) configurations revealed significantly superior accuracy to the 1 × 16 (116 ± 62%) configuration.

Conclusions

EIT-cystovolumetry compares well with ultrasound techniques. For estimation of RU, the selection of the EIT electrode configuration is important. Also, the development of an algorithm should consider the impact of movement artefacts. Finally, the accuracy of non-invasive ultrasound accepted as gold standard of cystovolumetry should be reconsidered.

Continuous bladder volume monitoring system for wearable applications

In this research, we propose a bladder volume monitoring system that can be effectively applied for various voiding dysfunctions. Whereas conventional systems lack consecutive measurements, the proposed system can continuously monitor a user's status even during unconscious sleep. For the convenience, we design a simple and comfortable waist-belt-type device by using the body impedance analysis (BIA) technique. To support various measurement scenarios, we develop applications by connecting the device to a smartphone. To minimize motion noises, which are inevitable when monitoring over an extended period, we propose a motion artifact reduction algorithm that exploits multiple frequency sources. The experimental results show a strong relationship between the impedance variation and the bladder volume; this confirms the feasibility of our system.

Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection

Urinary Incontinence affects over 200 million people worldwide, severely impacting the quality of life of individuals. Bladder state detection technology has the potential to improve the lives of people with urinary incontinence by alerting the user before voiding occurs. To this end, the objective of this study is to investigate the feasibility of using supervised machine learning classifiers to determine the bladder state of ‘full’ or ‘not full’ from electrical impedance measurements. Electrical impedance data was obtained from computational models and a realistic experimental pelvic phantom. Multiple datasets with increasing complexity were formed for varying noise levels in simulation. 10-Fold testing was performed on each dataset to classify ‘full’ and ‘not full’ bladder states, including phantom measurement data. Support vector machines and k-Nearest-Neighbours classifiers were compared in terms of accuracy, sensitivity, and specificity. The minimum and maximum accuracies across all datasets were 73.16% and 100%, respectively. Factors that contributed most to misclassification were the noise level and bladder volumes near the threshold of ‘full’ or ‘not full’. This paper represents the first study to use machine learning for bladder state detection with electrical impedance measurements. The results show promise for impedance-based bladder state detection to support those living with urinary incontinence.

A realistic pelvic phantom for electrical impedance measurement

OBJECTIVE

To design and fabricate an anatomically and conductively accurate phantom for electrical impedance studies of non-invasive bladder volume monitoring.

APPROACH

A modular pelvic phantom was designed and fabricated, consisting of a mechanically and conductively stable boundary wall, a background medium, and bladder phantoms. The wall and bladders are made of conductive polyurethane. The background material is an ultrasound gel-based mixture, with conductivity matched to a weighted average of the pelvic cavity organs, bone, muscle and fat. The phantom boundary is developed using a computer tomography model of a male human pelvis. The bladder phantoms were designed to correlate with human bladder dimensions. Electrical impedance measurements of the phantom were recorded, and images produced using six different bladder phantoms and a realistic finite element model.

MAIN RESULTS

Five different bladder volumes were successfully imaged using an empty bladder as a reference. The average conductivity index from the reconstructed images showed a strong positive correlation with the bladder phantom volumes.

SIGNIFICANCE

A conductively and anatomically accurate pelvic phantom was developed for non-invasive bladder volume monitoring using electrical impedance measurements. Several bladders were designed to correlate with actual human bladder volumes, allowing for accurate volume estimation. The conductivity of the phantom is accurate over 50-250 kHz. This phantom can allow changeable electrode location, contact and size; multi-layer electrodes configurations; increased complexity by addition of other organ or bone phantoms; and electrode movement and deformation. Overall, the pelvic phantom enables greater scope for experimentation and system refinement as a precursor to in-man clinical studies.

A prospective study of examining physiological signals for estimating occurrence of nocturnal enuresis

The nocturnal enuresis is challenging due to the increased social activities of the children. This disorder significantly bothers both the children and their parents in psychological, behavioral, social, and financial manners. However, the primary treatments have limitations and further are not able to completely cure the disorder. In order to reduce pain and burdens of patients and their parents, it is important to accurately estimate when the enuretic incident occurs in advance. For the estimation, we have comprehensively investigated various studies of the nocturnal enuresis in the diverse fields. Through the investigations, we have summarized four hypotheses of the physiological signals related to the enuretic moment. In order to conquer the nocturnal enuresis, we design a preliminary framework sensing and investigating the physiological signals with the sensors. Our synthesized approach to understand and estimate the moments of the enuretic incidents can establish a foothold to complete the promising prediction system.