ABSTRACT
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 pre-void 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.
Conclusions
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.
Keywords
Abbreviations:
DO (Detrusor Overactivity), UDS (Urodynamics), DC (Detrusor Contractions), LUTS (Lower Urinary Tract Symptoms), FFT (Fast Fourier Transform), SP (Spectral Power), WAF (Weighted Average Frequency), SUI (Stress Urinary Incontinence), OAB (Overactive Bladder)To read this article in full you will need to make a payment
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Article info
Publication history
Accepted:
January 11,
2023
Received:
August 31,
2022
Publication stage
In Press Journal Pre-ProofFootnotes
Declarations of interest: None
Ethics Approval: This study abides by the ethics and integrity policies of Urology IRB exception was obtained. IRB Case #: STUDY00008313
Funding Source: None
Identification
Copyright
© 2023 Elsevier Inc. All rights reserved.
