Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Orlandi, Silvia (2015) Non Invasive Tools for Early Detection of Autism Spectrum Disorders , [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Bioingegneria, 27 Ciclo. DOI 10.6092/unibo/amsdottorato/7149.
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Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

Tipologia del documento
Tesi di dottorato
Orlandi, Silvia
Dottorato di ricerca
Scuola di dottorato
Scienze e ingegneria dell'informazione
Settore disciplinare
Settore concorsuale
Parole chiave
Autism Spectrum Disorders Neurodevelopmental Disorders Audio/Video Acquisition System Contact-less Computer-Aided Diagnosis Automatic Infant Cry Analysis Parametric Spectral Estimation Fundamental Frequency Resonance Frequencies Cry Melody Newborn General Movements Analysis
Data di discussione
8 Maggio 2015

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