arXiv subjects

Margot S. Damaser

Publications and source records attributed to Margot S. Damaser.

2 recordsLinked to original sources

arXiv2026

Event-Based Estimation for Detrusor Pressure Using a Single Bladder Catheter Augmented with Abdominal Electromyography

Objective: The aim of this study was to enhance patient comfort and diagnostic accuracy in urodynamic studies (UDS) by developing a novel framework that uses a single vesical pressure (Pves) channel combined with a surface abdominal electromyography (AEMG) electrode array to estimate detrusor pressure (Pdet) without the need for an abdominal catheter. The proposed method was designed to operate in real-time, and to be compatible with current UDS clinical hardware. Methods: 40 UDS studies were performed with AEMG measured simultaneously with both Pves and Pabd. A predefined, provocative abdominal maneuver was used before each study to ensure accurate coupling between the AEMG signal and associated abdominal pressures seen in Pves. Criteria were set to determine if coupling was sufficient for Pdet estimation from Pves and AEMG. Post-hoc signal processing was used to further refine and evaluate estimated Pdet using automated, non-causal event detection. An event-based scoring metric was used to determine how accurately this approach estimated Pdet by calculating the percentage of signal contained in the appropriate channel for that event (Pdet vs abdominal pressure). Results: The results showed that 30 of the 40 cases indicated a "Go" status after the provocative maneuver, demonstrating feasibility of this method in a majority of UDS studies. The scoring metric, along with correlation analysis, confirmed accuracy of estimated Pdet with scores ranging from 85% to 95% with median at 93%. Significance: This innovative approach has the potential to revolutionize urodynamic studies by providing a more comfortable and accurate diagnostic tool for assessing bladder function. This is particularly beneficial for vulnerable populations, such as the elderly and children, reducing the need for multiple catheters and improving the overall patient experience during UDS.

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arXiv2026

Automated Detection of Urological Events in Bladder Pressure Signals with a Two-Stage Machine Learning Framework Validated on External Datasets

Objective: Conventional urodynamics (UDS) provide critical diagnostic information, but requires invasive dual catheterization and manual labeling of clinically important events. Wireless, catheter-free bladder function tests are becoming available for home use, but only provide vesical pressure (Pves). We developed a machine learning framework that was trained and externally validated on UDS data for automated urological event classification from single-channel (Pves) recordings. Methods: We analyzed 118 annotated UDS traces segmented into 0.8-second Pves intervals. Using the discrete wavelet transform, we extracted 55 statistical features per segment. Consecutive segments (233,338 segments; three classes) sharing the same class, abdominal (ABD), detrusor overactivity (DO), or voiding contraction (VOID), were grouped into events, and median feature aggregation was applied to derive event-level representations. Using an imbalanced dataset, we trained a two-stage multilayer perceptron (MLP): Stage 1 distinguished VOID vs non-VOID, and Stage 2 classified non-VOID into ABD and DO. The model was trained on two independent datasets and externally validated on a third independent dataset. Additional cross-dataset training-validation permutations were performed to assess generalizability. Performance was evaluated using accuracy, F1-macro, sensitivity, specificity, and area under the curve (AUC). Results: Stage 1 (VOID vs. non-VOID) achieved 84% accuracy (balanced accuracy 76%), F1-macro 0.74, and AUC 0.85, while Stage 2 (ABD vs. DO) reached 90% accuracy (balanced accuracy 80%), F1-macro 0.80, and AUC 0.87. Permutation feature importance indicated that most features contributed meaningfully. Conclusion: Our machine learning approach enables accurate automated detection of urological events from Pves, demonstrating feasibility for single-channel monitoring and future ambulatory applications.

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