# Automatic cry analysis and classification for infant pain assessment

## TL;DR

This paper introduces a preliminary automatic cry analysis method for infant pain assessment, focusing on vocal features like duration, dysphonation, and fundamental frequency to classify distress levels with high correlation to human assessments.

## Contribution

It presents a novel spectral entropy-based measure for dysphonation and integrates multiple vocal indicators into an automatic pain scoring system for infants.

## Key findings

- Spectral entropy effectively measures cry dysphonation.
- Cry features correlate strongly with human pain assessments.
- Proposed indicators classify distress levels accurately.

## Abstract

The effectiveness of pain management relies on the choice and the correct use of suitable pain assessment tools. In the case of newborns, some of the most common tools are human-based and observational, thus affected by subjectivity and methodological problems. Therefore, in the last years there has been an increasing interest in developing an automatic machine-based pain assessment tool. This research is a preliminary investigation towards the inclusion of a scoring system for the vocal expression of the infant into an automatic tool. To this aim we present a method to compute three correlated indicators which measure three distress-related features of the cry: duration, dysphonantion and fundamental frequency of the first cry. In particular, we propose a new method to measure the dysphonantion of the cry via spectral entropy analysis, resulting in an indicator that identifies three well separated levels of distress in the vocal expression. These levels provide a classification that is highly correlated with the human-based assessment of the cry.