Cry-based detection of developmental disorders in infants

Developmental disorders are a group of neurological conditions originating in early neural development. These disorders involve serious impairments in language, learning, and motor skills. An early detection of developmental disorders is crucial, as it enables early intervention (e.g., speech-language and occupational therapy) that may reduce neurological and functional deficits. In this work, we propose a k-nearest neighbours (k-nn) classifier for an early identification of developmental disorders in infants based on their cry. The classifier is based on temporal and spectral features extracted from the cry signal, where the contribution of each feature is estimated in an optimization process. Performance is evaluated against a database of diagnosed infants, with 89% accuracy in cross-validation testing.