Abstract
BACKGROUND: Spinal cord injury (SCI) often leads to the loss of urinary sensation, making urination difficult. In a previous experiment involving six healthy participants, we measured heartbeat-induced acoustic pulse waves (HAPWs) at the mid-back, calculated time-series power spectra of heart rate gradients at three ultralow/very low frequencies, distinguished and formulated waveform characteristics (one characteristic for each power spectrum, nearly uniform across participants) at times of increased urine in the bladder and heightened urges to urinate, and developed an algorithm with five of these power spectra to identify when urination is needed by extracting the waveform portion (continuous timepoints) where all of the characteristics were consistent with the formulated characteristics. The objective of this study was to verify the validity of the algorithm fed with data from measured HAPW of participants with SCI and to adapt the algorithm for these individuals. METHODS: In ten participants with SCI, we measured HAPWs continuously and urine volume intermittently, and obtained scores related to urinary sensation. A Boolean output at each data point was obtained by the algorithm fed with the calculated power spectra from each participant's HAPW. Notable times included when the output was positive or when the need to urinate (= ( +)) was judged from the urine volume and urinary sensation scores. The outputs at these notable times were examined with the need to urinate and determined to be true/false. The accuracy of the algorithm was evaluated by the number of true/false-positive/negative points via the F-score with a binary classification model. We attempted to adapt the algorithm for participants with SCI. RESULTS: The outputs at 13 notable times were examined, yielding seven true-positive, one false-positive, and five false-negative times, with an F-score of 0.70. The algorithm was modified by replacing three thresholds that determine the extraction condition for the slope in the power spectral waveform with new values that included all 12 true-positive points. CONCLUSIONS: Without changing the use of ultralow/very low frequencies or significantly modifying the extraction conditions, the modified algorithm did not miss any true urination times or identify false urination times in ten participants with SCI.