Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

This research work introduces a novel, nonintrusive method for the automatic identification of Smith–Magenis syndrome, traditionally studied through genetic markers. The method utilizes cepstral peak prominence and various machine learning techniques, relying on a single metric computed by the resea...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän: Raúl Fernández-Ruiz, Esther Núñez-Vidal, Irene Hidalgo-delaguía, Elena Garayzábal-Heinze, Agustín Álvarez-Marquina, Rafael Martínez-Olalla, Daniel Palacios-Alonso
Materialtyp: Artikel
Språk:English
Publicerad: Frontiers Media S.A. 2024-03-01
Serie:Frontiers in Computational Neuroscience
Ämnen:
Smith–Magenis syndrome
machine learning
cepstral peak prominence
acoustics
children
Länkar:https://www.frontiersin.org/articles/10.3389/fncom.2024.1357607/full

Internet

https://www.frontiersin.org/articles/10.3389/fncom.2024.1357607/full

Liknande verk