Optimization of activity-driven event detection for long-term ambulatory urodynamics.

Lower urinary tract dysfunction (LUTD) is a debilitating condition that affects millions of individuals worldwide, greatly diminishing their quality of life. The use of wireless, catheter-free implantable devices for long-term ambulatory bladder monitoring, combined with a single-sensor system capable of detecting various bladder events, has the potential to significantly enhance the diagnosis and treatment of LUTD. However, these systems produce large amounts of bladder data that may contain physiological noise in the pressure signals caused by motion artifacts and sudden movements, such as coughing or laughing, potentially leading to false positives during bladder event classification and inaccurate diagnosis/treatment. Integration of activity recognition (AR) can improve classification accuracy, provide context regarding patient activity, and detect motion artifacts by identifying contractions that may result from patient movement. This work investigates the utility of including data from inertial measurement units (IMUs) in the classification pipeline, and considers various digital signal processing (DSP) and machine learning (ML) techniques for optimization and activity classification. In a case study, we analyze simultaneous bladder pressure and IMU data collected from an ambulating female Yucatan minipig. We identified 10 important, yet relatively inexpensive to compute signal features, with which we achieve an average 91.5% activity classification accuracy. Moreover, when classified activities are included in the bladder event analysis pipeline, we observe an improvement in classification accuracy, from 81% to 89.0%. These results suggest that certain IMU features can improve bladder event classification accuracy with low computational overhead.Clinical Relevance: This work establishes that activity recognition may be used in conjunction with single-channel bladder event detection systems to distinguish between contractions and motion artifacts for reducing the incorrect classification of bladder events. This is relevant for emerging sensors that measure intravesical pressure alone or for data analysis of bladder pressure in ambulatory subjects that contain significant abdominal pressure artifacts.

Detrusor Pressure Estimation from Single-Channel Urodynamics.

Urodynamics is the current gold-standard for diagnosing lower urinary tract dysfunction, but uses non-physiologically fast, retrograde cystometric filling to obtain a brief snapshot of bladder function. Ambulatory urodynamics allows physicians to evaluate bladder function during natural filling over longer periods of time, but artifacts generated from patient movement necessitate the use of an abdominal pressure sensor, which makes long-term monitoring and feedback for closed-loop treatment impractical. In this paper, we analyze the characteristics of single-channel bladder pressure signals from human and feline datasets, and present an algorithm designed to estimate detrusor pressure, which is useful for diagnosis and treatment. We utilize multiresolution analysis techniques to maximize the attenuation of probable abdominal pressure components in the vesical pressure signal. Results indicate a strong correlation, averaging 0.895 ± 0.121 (N = 40) and 0.812 ± 0.113 (N = 16) between the estimated detrusor pressure obtained by the proposed method and recorded urodynamic data from human and feline subjects, respectively. Clinical Relevance- This work establishes that signal pro-cessing techniques may be applied to vesical pressure alone to accurately reconstruct pressures generated independently by the detrusor muscle. This is relevant for emerging sensors that measure vesical pressure alone or for data analysis of bladder pressure in ambulatory subjects which contains significant abdominal pressure artifacts.

Tunable and Lightweight On-Chip Event Detection for Implantable Bladder Pressure Monitoring Devices.

Lower urinary tract dysfunctions, such as urinary incontinence and overactive bladder, are conditions that greatly affect the quality of life for millions of individuals worldwide. For those with more complex pathophysiologies, diagnosis of these conditions often requires a urodynamics study, providing physicians with a snapshot view of bladder mechanics. Recent advancements in implantable bladder pressure monitors and advanced data analysis techniques have made diagnosis through chronic monitoring a promising prospect. However, implants targeted at treatment must remain in the bladder for long periods of time, making minimizing power consumption a primary design objective. Currently, much of the typical implant's power draw is due to data transmission. Previous work has demonstrated an adaptive rate transmission technique to reduce power consumption. However, the ultimate reduction in power consumption can only be attained when the device does not transmit bladder pressure samples, but rather bladder events. In this paper, we present an algorithm and circuit level implementation for on-chip bladder pressure data compression and event detection. It is designed to be a complete, tunable, and lightweight diagnosis and treatment framework for bladder pressure monitoring implants, capable of selectively transmitting compressed bladder pressure data with tunable quality, "snapshots" of significant bladder events, or simply indicate events occurred for the highest energy efficiency. The design aims to minimize area through resource reuse, leading to a total area of 1.75 , and employs advanced VLSI techniques for power reduction. With compression and event detection enabled, the design consumes roughly 2.6 nW in TSMC technology. With only event detection, this reduces to 2.1 nW, making this approach ideal for long-life implantable bladder pressure monitoring devices.

Sustainable Cost Models for mHealth at Scale: Modeling Program Data from m4RH Tanzania.

BACKGROUND: There is increasing evidence that mobile phone health interventions ("mHealth") can improve health behaviors and outcomes and are critically important in low-resource, low-access settings. However, the majority of mHealth programs in developing countries fail to reach scale. One reason may be the challenge of developing financially sustainable programs. The goal of this paper is to explore strategies for mHealth program sustainability and develop cost-recovery models for program implementers using 2014 operational program data from Mobile for Reproductive Health (m4RH), a national text-message (SMS) based health communication service in Tanzania. METHODS: We delineated 2014 m4RH program costs and considered three strategies for cost-recovery for the m4RH program: user pay-for-service, SMS cost reduction, and strategic partnerships. These inputs were used to develop four different cost-recovery scenarios. The four scenarios leveraged strategic partnerships to reduce per-SMS program costs and create per-SMS program revenue and varied the structure for user financial contribution. Finally, we conducted break-even and uncertainty analyses to evaluate the costs and revenues of these models at the 2014 user volume (125,320) and at any possible break-even volume. RESULTS: In three of four scenarios, costs exceeded revenue by $94,596, $34,443, and $84,571 at the 2014 user volume. However, these costs represented large reductions (54%, 83%, and 58%, respectively) from the 2014 program cost of $203,475. Scenario four, in which the lowest per-SMS rate ($0.01 per SMS) was negotiated and users paid for all m4RH SMS sent or received, achieved a $5,660 profit at the 2014 user volume. A Monte Carlo uncertainty analysis demonstrated that break-even points were driven by user volume rather than variations in program costs. CONCLUSIONS: These results reveal that breaking even was only probable when all SMS costs were transferred to users and the lowest per-SMS cost was negotiated with telecom partners. While this strategy was sustainable for the implementer, a central concern is that health information may not reach those who are too poor to pay, limiting the program's reach and impact. Incorporating strategies presented here may make mHealth programs more appealing to funders and investors but need further consideration to balance sustainability, scale, and impact.

Real-time, autonomous bladder event classification and closed-loop control from single-channel pressure data.

Urinary incontinence, or the loss of bladder control, is a debilitating condition affecting millions worldwide, which significantly reduces quality of life. Neuromodulation of lower urinary tract nerves can be used to treat sensations of urgency in many subjects, including those with Spinal Cord Injury (SCI). Event driven, or conditional stimulation has been investigated as a possible improvement to the state-of-the-art open-loop stimulation systems available today. However, this requires a robust, adaptive, and noise-tolerant method of classifying bladder function from real-time bladder pressure measurements. Context-Aware Thresholding (CAT) has been previously shown to work well on prerecorded single contraction urodynamic data. In this work, for the first time, we present real-time detection of multiple serial bladder contractions using urodynamic recordings from human subjects with SCI and Neurogenic Detrusor Overactivity (NDO). CAT demonstrated a high degree of accuracy and noise tolerance on prerecorded data from 15 human subjects, with a mean accuracy of 92% and average false positive rate of 0.3 false positives per contraction. Analysis of event detection latencies showed that CAT identified and responded to events 1.4 seconds faster than the original human experimenter. Finally, we present a case study in which CAT was used live for real-time autonomous, closed-loop bladder control in a single human subject with SCI and NDO, successfully inhibiting four consecutive unwanted bladder contractions and increasing bladder capacity by 40%.

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

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.

Effects of the Fataki campaign: addressing cross-generational sex in Tanzania by mobilizing communities to intervene.

The national multimedia "Fataki" campaign aired in Tanzania from 2008 to 2011 with the goal of addressing cross-generational sex (CGS) by mobilizing communities to intervene in CGS relationships. A cross-sectional household survey was used to evaluate the campaign. Logistic regression analysis found a dose-response relationship between campaign exposure and interpersonal communication about CGS, intervening in CGS relationships, and lower CGS engagement among women. No association was found between campaign exposure and current CGS involvement among men, though longer-term data collection may be needed to assess changes in relationship patterns. Findings indicated that engaging in interpersonal communication about CGS was associated with a higher likelihood of actually intervening. Strategies to generate further discussion surrounding CGS and increase impact, such as through community-based components to supplement campaigns, are discussed.