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This research was funded by Agencia Estatal De Investigacion from Spain, grant number PID2023-152984OB-I00 and PID2021-124176OB-I00.

Analysis of institutional authors

Fernandez-Ruiz, RaulAuthorDominguez-Mateos, FranciscoAuthorConde, CristinaAuthor

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November 19, 2024
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Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification

Publicated to:Applied Sciences-Basel. 14 (21): 9747- - 2024-11-01 14(21), DOI: 10.3390/app14219747

Authors: Nunez-Vidal, Esther; Fernandez-Ruiz, Raul; Alvarez-Marquina, Agustin; Hidalgo-delaGuia, Irene; Garayzabal-Heinze, Elena; Hristov-Kalamov, Nikola; Dominguez-Mateos, Francisco; Conde, Cristina; Martinez-Olalla, Rafael

Affiliations

Univ Autonoma Madrid, Dept Linguist, Madrid 28049, Spain - Author
Univ Complutense Madrid, Dept Spanish Language & Theory Literature, Madrid 28040, Spain - Author
Univ Politecn Madrid, Ctr Biomed Technol, Madrid 28220, Spain - Author
Univ Rey Juan Carlos, Escuela Tecn Super Ingn Informat, C Tulipan S-N, Mostoles 28933, Spain - Author

Abstract

Smith-Magenis syndrome (SMS) is a rare, underdiagnosed condition due to limited public awareness of genetic testing and a lengthy diagnostic process. Voice analysis can be a noninvasive tool for monitoring and detecting SMS. In this paper, the cepstral peak prominence and mel-frequency cepstral coefficients are used as disease monitoring and detection metrics. In addition, an efficient neural network, incorporating synthetic data processes, was used to detect SMS in a cohort of individuals with the disease. Three study cases were conducted with a set of 19 SMS patients and 292 controls. The three study cases employed various oversampling and undersampling techniques, including SMOTE, random oversampling, NearMiss, random undersampling, and 16 additional methods, resulting in balanced accuracies ranging from 69% to 92%. This is the first study using a neural network model to focus on a rare genetic syndrome using phonation analysis data. By using synthetic data (oversampling and undersampling) and a CNN, it was possible to detect SMS with high levels of accuracy. Voice analysis and deep learning techniques have proven to be a useful and noninvasive method. This is a finding that may help in the complex identification of this syndrome as well as other rare diseases.

Keywords

17p11.2 deletionsArticulationChildrenCnnDeep learningDysarthriaFeatureIntelligibilityMutationsParkinsons-diseasePhenotypePhonationSmith-magenis syndromeSmith–magenis syndromeSpeechSynthetic datSynthetic data

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Sciences-Basel due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 50/175, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Multidisciplinary. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Engineering (Miscellaneous).

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-04:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 6.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.25.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author () .

the author responsible for correspondence tasks has been .