Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

ABSTRACT:

Background

To characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.

Methods

Assessment of 1000 cries in a mobile app (ChatterBaby^TM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.

Results

The cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The Chat

SUBMITTER: Parga JJ 

PROVIDER: S-EPMC7033040 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature