| Titre : | Automated cortical auditory response detection strategy (2020) |
| Auteurs : | Fabrice Bardy ; Bram Van Dun ; Mark Seeto ; Harvey Dillon |
| Type de document : | Article |
| Dans : | International Journal of Audiology IJA (Vol. 59, n°11, Novembre 2020) |
| Article en page(s) : | p. 835-842 |
| Langues: | Anglais |
| Descripteurs : |
HE Vinci Algorithmes ; Electrophysiologie auditive ; Potentiel évoqué cortical sonore (CAEP) |
| Résumé : |
Objective: This study describes a new automated strategy to determine the detection status of an electrophysiological response.
Design: Response, noise and signal-to-noise ratio of the cortical auditory evoked potential (CAEP) were characterised. Detection rules were defined: when to start testing, when to conduct subsequent statistical tests using residual noise as an objective criterion, and when to stop testing. Study sample: Simulations were run to determine optimal parameters on a large combined CAEP data set collected in 45 normal-hearing adults and 17 adults with hearing loss. Results: The proposed strategy to detect CAEPs is fully automated. The first statistical test is conducted when the residual noise level is equal to or smaller than 5.1 µV. The succeeding Hotelling’s T2 statistical tests are conducted using pre-defined residual noise levels criteria ranging from 5.1 to 1.2 µV. A rule was introduced allowing to stop testing before the maximum number of recorded epochs is reached, depending on a minimum p-value criterion. Conclusion: The proposed framework can be applied to systems which involves detection of electrophysiological responses in biological systems containing background noise. The proposed detection algorithm which optimise sensitivity, specificity, and recording time has the potential to be used in clinical setting. |
| Accès : | Contactez la bibliothèque d'Ixelles si le lien vers la ressource électronique ne fonctionne plus |
| Disponible en ligne : | Oui |
| En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.tandfonline.com/doi/pdf/10.1080/14992027.2020.1767808?needAccess=true |
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