Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations
Quickly and efficiently extracting evoked potential information from noise is critical to the clinical practice of intraoperative neurophysiologic monitoring (IONM). Currently this is primarily done using trained professionals to interpret averaged waveforms. The purpose of this paper is to evaluate...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023058796 |
| _version_ | 1827856230369984512 |
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| author | Mark M. Stecker Jonathan Wermelinger Jay Shils |
| author_facet | Mark M. Stecker Jonathan Wermelinger Jay Shils |
| author_sort | Mark M. Stecker |
| collection | DOAJ |
| description | Quickly and efficiently extracting evoked potential information from noise is critical to the clinical practice of intraoperative neurophysiologic monitoring (IONM). Currently this is primarily done using trained professionals to interpret averaged waveforms. The purpose of this paper is to evaluate and compare multiple means of electronically extracting simple to understand evoked potential characteristics with minimum averaging. A number of evoked potential models are studied and their performance evaluated as a function of the signal to noise level in simulations. Methods: which extract the least number of parameters from the data are least sensitive to the effects of noise and are easiest to interpret. The simplest model uses the baseline evoked potential and the correlation receiver to provide an amplitude measure. Amplitude measures extracted using the correlation receiver show superior performance to those based on peak to peak amplitude measures. In addition, measures of change in latency or shape of the evoked potential can be extracted using the derivative of the baseline evoked response or other methods. This methodology allows real-time access to amplitude measures that can be understood by the entire OR staff as they are small, dimensionless numbers of order unity which are simple to interpret. The IONM team can then adjust averaging and other parameters to allow for visual interpretation of waveforms as appropriate. |
| first_indexed | 2024-03-12T12:21:47Z |
| format | Article |
| id | doaj.art-8d27e475414a40ddbebf3fc696af04ff |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T12:21:47Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-8d27e475414a40ddbebf3fc696af04ff2023-08-30T05:52:10ZengElsevierHeliyon2405-84402023-08-0198e18671Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulationsMark M. Stecker0Jonathan Wermelinger1Jay Shils2Fresno Institute of Neuroscience, USA; Corresponding author. 1968 S. Coast Hwy Suite 550 Laguna Beach CA 926511, USA. Tel./Fax: 516-478-8304.Neurosurgery Department, Inselspital, University Hospital Bern, SwitzerlandDepartment of Neurosurgery, Rush College of Medicine, USAQuickly and efficiently extracting evoked potential information from noise is critical to the clinical practice of intraoperative neurophysiologic monitoring (IONM). Currently this is primarily done using trained professionals to interpret averaged waveforms. The purpose of this paper is to evaluate and compare multiple means of electronically extracting simple to understand evoked potential characteristics with minimum averaging. A number of evoked potential models are studied and their performance evaluated as a function of the signal to noise level in simulations. Methods: which extract the least number of parameters from the data are least sensitive to the effects of noise and are easiest to interpret. The simplest model uses the baseline evoked potential and the correlation receiver to provide an amplitude measure. Amplitude measures extracted using the correlation receiver show superior performance to those based on peak to peak amplitude measures. In addition, measures of change in latency or shape of the evoked potential can be extracted using the derivative of the baseline evoked response or other methods. This methodology allows real-time access to amplitude measures that can be understood by the entire OR staff as they are small, dimensionless numbers of order unity which are simple to interpret. The IONM team can then adjust averaging and other parameters to allow for visual interpretation of waveforms as appropriate.http://www.sciencedirect.com/science/article/pii/S2405844023058796Evoked potentialAmplitudeLeast squaresCorrelationReceiverLatency |
| spellingShingle | Mark M. Stecker Jonathan Wermelinger Jay Shils Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations Heliyon Evoked potential Amplitude Least squares Correlation Receiver Latency |
| title | Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations |
| title_full | Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations |
| title_fullStr | Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations |
| title_full_unstemmed | Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations |
| title_short | Efficient extraction of data from intra-operative evoked potentials: 1.-Theory and simulations |
| title_sort | efficient extraction of data from intra operative evoked potentials 1 theory and simulations |
| topic | Evoked potential Amplitude Least squares Correlation Receiver Latency |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023058796 |
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