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Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome.
Pokorny, Florian B; Schmitt, Maximilian; Egger, Mathias; Bartl-Pokorny, Katrin D; Zhang, Dajie; Schuller, Björn W; Marschik, Peter B.
Afiliação
  • Pokorny FB; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria. florian.pokorny@medunigraz.at.
  • Schmitt M; Machine Intelligence & Signal Processing group (MISP), Technical University of Munich, Munich, Germany. florian.pokorny@medunigraz.at.
  • Egger M; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany. florian.pokorny@medunigraz.at.
  • Bartl-Pokorny KD; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
  • Zhang D; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria.
  • Schuller BW; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria.
  • Marschik PB; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
Sci Rep ; 12(1): 13345, 2022 08 03.
Article em En | MEDLINE | ID: mdl-35922535
Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)-(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndrome de Rett / Síndrome do Cromossomo X Frágil Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans / Infant Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndrome de Rett / Síndrome do Cromossomo X Frágil Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans / Infant Idioma: En Ano de publicação: 2022 Tipo de documento: Article