Real-Time Classification of Bladder Events for Effective Diagnosis and Treatment of Urinary Incontinence

• Published in: IEEE transactions on biomedical engineering Vol. 63; no. 4; pp. 721 - 729
• Main Authors: Karam, Robert, Bourbeau, Dennis, Majerus, Steve, Makovey, Iryna, Goldman, Howard B., Damaser, Margot S., Bhunia, Swarup
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Approximation methods; Bladder; Closed-loop Control; Endoscopy - instrumentation; Energy efficiency; Equipment Design; Event Detection Algorithms; Hardware; Humans; Incontinence; Overactive Bladder; Real-time systems; Robotics - instrumentation; Sensitivity; Sensors; Signal Processing, Computer-Assisted - instrumentation; Spinal Cord Injury; Urinary Bladder - physiopathology; Urinary Dysfunction; Urinary incontinence; Urinary Incontinence - diagnosis; Urinary Incontinence - physiopathology; spinal cord injury; incontinence; overactive bladder; urinary dysfunction; event detection algorithms; Closed-loop control
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2015.2469604

Diagnosis of lower urinary tract dysfunction with urodynamics has historically relied on data acquired from multiple sensors using nonphysiologically fast cystometric filling. In addition, state-of-the-art neuromodulation approaches to restore bladder function could benefit from a bladder sensor for closed-loop control, but a practical sensor and automated data analysis are not available. We have developed an algorithm for real-time bladder event detection based on a single in situ sensor, making it attractive for both extended ambulatory bladder monitoring and closed-loop control of stimulation systems for diagnosis and treatment of bladder overactivity. Using bladder pressure data acquired from 14 human subjects with neurogenic bladder, we developed context-aware thresholding, a novel, parameterized, user-tunable algorithmic framework capable of real-time classification of bladder events, such as detrusor contractions, from single-sensor bladder pressure data. We compare six event detection algorithms with both single-sensor and two-sensor systems using a metric termed Conditional Stimulation Score, which ranks algorithms based on projected stimulation efficacy and efficiency. We demonstrate that adaptive methods are more robust against day-to-day variations than static thresholding, improving sensitivity and specificity without parameter modifications. Relative to other methods, context-aware thresholding is fast, robust, highly accurate, noise-tolerant, and amenable to energy-efficient hardware implementation, which is important for mapping to an implant device.