A Phase-Based Electrical Plethysmography Approach to Bladder Volume Measurement

Abstract

Neuromodulation approaches to treating lower urinary tract dysfunction could be substantially improved by a sensor able to detect when the bladder is full. A number of approaches to this problem have been proposed, but none has been found entirely satisfactory. Electrical plethysmography approaches attempt to relate the electrical impedance of the bladder to its volume, but have previously focused only on the amplitudes of the measured signals. We investigated whether the phase relationships between sinusoidal currents applied through a pair of stimulating electrodes and measured through a pair of recording electrodes could provide information about bladder volume. Acute experiments in a rabbit model were used to investigate how phase-to-volume or amplitude-to-volume regression models could be used to predict bladder volumes in future recordings, with and without changes to the saline conductivity. Volume prediction errors were found to be 6.63 ± 1.12 mL using the phase information and 8.32 ± 3.88 mL using the amplitude information (p = 0.44 when comparing the phase and amplitude results, n = 6), where the volume of the filled bladder was about 25 mL. When a full/empty binary decision rule was applied based on the regression model, the difference between the actual threshold that would result from this rule and the desired threshold was found to be 4.24 ± 0.65 mL using the phase information and 106.92 ± 189.82 mL using the amplitude information (p = 0.03, n = 6). Our results suggest that phase information can form the basis for more effective and robust electrical plethysmography approaches to bladder volume measurement.

A realistic pelvic phantom for electrical impedance measurement

“…Bladder volume monitoring using electrical impedance (EI) techniques has been studied in simulation , with simplified tank-based measurements (He et al, 2012;, on animals (Denniston and Baker, 1975;Zariffa et al, 2016), and on humans (Kim et al, 1998;Leonhardt et al, 2011;Liao and Jaw, 2011;Zariffa et al, 2016, Shin, et al, 2017. Human studies have shown strong correlation of bladder volume with the processed EI data (Leonhardt et al, 2011;Li et al, 2016), highlighting the feasibility of EI to continuously monitor bladder volume in clinical situations.…”

Supervised Learning Classifiers for Electrical Impedance-based Bladder State Detection

“…Urinary incontinence (UI) is the involuntarily discharge of urine 1 , and it affects over 200 million people worldwide 2 . The condition may develop in an individual due to advancing age 3 , 4 , urinary tract infections 3 , diabetes 3 , 5 , or due to a neurological disease such as stroke, spinal cord injury, or multiple sclerosis 3 , 6 . Urinary Incontinence can be associated with urinary tract infections, urinary reflux and even renal damage and failure 7 , 8 .…”

“… 7 and Zariffa et al . 6 . The results from these studies have shown a strong correlation between the processed electrical impedance data and the bladder volume.…”

Linear Regression for Estimating Bladder Volume with Voltage Signals

“…Urinary incontinence affects over 200 million people throughout the world [1]. In particular the condition affects children and adolescents with intellectual disabilities [2], the elderly [3], [4] and those with neurological conditions [5]. Urinary incontinence can severely impact the quality of life of these individuals [6], [7].…”