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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...

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Autores principales: Manta, Ourania, Sarafidis, Michail, Vasileiou, Nikolaos, Schlee, Winfried, Consoulas, Christos, Kikidis, Dimitris, Vassou, Evgenia, Matsopoulos, George K., Koutsouris, Dimitrios D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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