Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals
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 in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2016-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2016-0043 |
| _version_ | 1818606462312644608 |
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| author | Wegner Celine Krueger Thilo B. Hoffmann Klaus-Peter Kauff Daniel W. Kneist Werner |
| author_facet | Wegner Celine Krueger Thilo B. Hoffmann Klaus-Peter Kauff Daniel W. Kneist Werner |
| author_sort | Wegner Celine |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T14:11:14Z |
| format | Article |
| id | doaj.art-03a7dc337c1e4c2d8159dca9764dc48d |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-12-16T14:11:14Z |
| publishDate | 2016-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-03a7dc337c1e4c2d8159dca9764dc48d2022-12-21T22:28:45ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042016-09-012118919210.1515/cdbme-2016-0043cdbme-2016-0043Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signalsWegner Celine0Krueger Thilo B.1Hoffmann Klaus-Peter2Kauff Daniel W.3Kneist Werner4Inomed Medizintechnik GmbH, Im Hausgrün 29, 79312 Emmendingen, GermanyInomed Medizintechnik GmbH, Emmendingen, GermanyFraunhofer Institute for Biomedical Engineering, St. Ingbert, GermanyDepartment of General, Visceral and Transplant Surgery, University Medical Center, University Medicine of the Johannes Gutenberg-University Mainz, GermanyDepartment of General, Visceral and Transplant Surgery, University Medical Center, University Medicine of the Johannes Gutenberg-University Mainz, GermanyTwo 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.https://doi.org/10.1515/cdbme-2016-0043pelvic intraoperative neuromonitoringpostprocessing algorithmsignal analysis |
| spellingShingle | Wegner Celine Krueger Thilo B. Hoffmann Klaus-Peter Kauff Daniel W. Kneist Werner Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals Current Directions in Biomedical Engineering pelvic intraoperative neuromonitoring postprocessing algorithm signal analysis |
| title | Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| title_full | Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| title_fullStr | Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| title_full_unstemmed | Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| title_short | Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| title_sort | postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals |
| topic | pelvic intraoperative neuromonitoring postprocessing algorithm signal analysis |
| url | https://doi.org/10.1515/cdbme-2016-0043 |
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