Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals

Abstract

Two dimensional pelvic intraoperative neuromonitoring (pIONM®) is based on electric stimulation of autonomic nerves under observation of electromyography of internal anal sphincter (IAS) and manometry of urinary bladder. The method provides nerve identification and verification of its’ functional integrity. Currently pIONM® is gaining increased attention in times where preservation of function is becoming more and more important. Ongoing technical and methodological developments in experimental and clinical settings require further analysis of the obtained signals. This work describes a postprocessing algorithm for pIONM® signals, developed for automated analysis of huge amount of recorded data. The analysis routine includes a graphical representation of the recorded signals in the time and frequency domain, as well as a quantitative evaluation by means of features calculated from the time and frequency domain. The produced plots are summarized automatically in a PowerPoint presentation. The calculated features are filled into a standardized Excel-sheet, ready for statistical analysis.

Keywords

• pelvic intraoperative neuromonitoring
• postprocessing algorithm
• signal analysis