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Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation
Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitude...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775187/ https://www.ncbi.nlm.nih.gov/pubmed/36552135 http://dx.doi.org/10.3390/brainsci12121675 |
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author | Manta, Ourania Sarafidis, Michail Vasileiou, Nikolaos Schlee, Winfried Consoulas, Christos Kikidis, Dimitris Vassou, Evgenia Matsopoulos, George K. Koutsouris, Dimitrios D. |
author_facet | Manta, Ourania Sarafidis, Michail Vasileiou, Nikolaos Schlee, Winfried Consoulas, Christos Kikidis, Dimitris Vassou, Evgenia Matsopoulos, George K. Koutsouris, Dimitrios D. |
author_sort | Manta, Ourania |
collection | PubMed |
description | Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools’ detection and annotation results, regarding the waves of interest, were then compared to the clinicians’ manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals. |
format | Online Article Text |
id | pubmed-9775187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97751872022-12-23 Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation Manta, Ourania Sarafidis, Michail Vasileiou, Nikolaos Schlee, Winfried Consoulas, Christos Kikidis, Dimitris Vassou, Evgenia Matsopoulos, George K. Koutsouris, Dimitrios D. Brain Sci Article Auditory evoked potentials (AEPs) are brain-derived electrical signals, following an auditory stimulus, utilised to examine any obstructions along the brain neural-pathways and to diagnose hearing impairment. The clinical evaluation of AEPs is based on the measurements of the latencies and amplitudes of waves of interest; hence, their identification is a prerequisite for AEP analysis. This process has proven to be complex, as it requires relevant clinical experience, and the existing software for this purpose has little practical use. The aim of this study was the development of two automated annotation tools for ABR (auditory brainstem response)- and AMLR (auditory middle latency response)-tests. After the acquisition of 1046 raw waveforms, appropriate pre-processing and implementation of a four-stage development process were performed, to define the appropriate logical conditions and steps for each algorithm. The tools’ detection and annotation results, regarding the waves of interest, were then compared to the clinicians’ manual annotation, achieving match rates of at least 93.86%, 98.51%, and 91.51% respectively, for the three ABR-waves of interest, and 93.21%, 92.25%, 83.35%, and 79.27%, respectively, for the four AMLR-waves. The application of such tools in AEP analysis is expected to assist towards an easier interpretation of these signals. MDPI 2022-12-06 /pmc/articles/PMC9775187/ /pubmed/36552135 http://dx.doi.org/10.3390/brainsci12121675 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Manta, Ourania Sarafidis, Michail Vasileiou, Nikolaos Schlee, Winfried Consoulas, Christos Kikidis, Dimitris Vassou, Evgenia Matsopoulos, George K. Koutsouris, Dimitrios D. Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title | Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title_full | Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title_fullStr | Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title_full_unstemmed | Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title_short | Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation |
title_sort | development and evaluation of automated tools for auditory-brainstem and middle-auditory evoked potentials waves detection and annotation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775187/ https://www.ncbi.nlm.nih.gov/pubmed/36552135 http://dx.doi.org/10.3390/brainsci12121675 |
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