Objective Quantification of Detrusor Overactivity Using Spectral Measures of Cystometry Data

Bhaskar Ravishankar, Ranveer Vasdev, Gerald W Timm, Sean Elliott, Nissrine A. Nakib, Matthew Johnson, Dwight E. Nelson

Research output: Contribution to journalArticlepeer-review


Objective: To develop scalable objective methods for differentiating patients with and without detrusor overactivity (DO) using quantitative Fast Fourier Transform (FFT)-based measures and routinely captured cystometry data. Methods: Retrospective cystometry data were collected as prevoid vesical and abdominal pressure signals from 18 DO and 10 SUI (non-DO) cystometry recordings. Data were filtered and divided into two equal-duration segments, Early and Late Fill, representing the first and second halves of filling. FFT was applied, followed by subtraction of abdominal spectra from vesical spectra. Spectral Power (SP) and Weighted Average Frequency (WAF) measures were calculated for each segment spectra within 1-6 cycles min−1. Results: Compared to non-DO, the mean SP was significantly higher in DO patients for both Early and Late Fill segments. WAF was significantly lower in DO patients for both segments. Changes in spectral pressures appeared to be linked to the presence of detrusor contractions (DCs) and were especially visible when DCs were present in the Early Fill segments of cystometry. Conclusion: FFT-based spectral measures derived from routinely captured cystometry data are significantly different between DO and non-DO patients. This preliminary method is clinically scalable and can be further developed to facilitate the detection of DO, classify disease phenotype, and capture therapeutic efficacy.

Original languageEnglish (US)
Pages (from-to)206-211
Number of pages6
StatePublished - Apr 2023

Bibliographical note

Funding Information:
Funding Support: This work is sponsored in part by funds from The Dr. Gerald W. Timm Endowed Professorship in Neurourology and Urologic Engineering, University of Minnesota School of Medicine.

Publisher Copyright:
© 2023 Elsevier Inc.


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