Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

Content-based classification of breath sound with enhanced features

Objective - Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach - For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts-full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results - The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance - Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

/pdf/video-and-audio-processing-in-paediatrics-a-review-2j1sru9in8.pdf

Video and audio processing in paediatrics: a review.

Crying is the infant’s first verbal communication. Before learning how to express the emotions or physiological/psychological requirements with language, infants usually express how they feel through crying. Crying is a response to a stimulus such as hunger, pain, or discomfort. However, it is sometimes difficult to figure out why an infant is crying. This can certainly be frustrating or even frightening for a caretaker, and so we in this paper have proposed an infant cry classification system to categorize the types of infant crying to help parents and nursing staffs attending to the needs of the infants. Currently, three kinds of distinct infant cries have been identified: hunger; pain; and feeling sleepy. Fifteen features are extracted from each crying frame and the sequential forward floating selection is then adopted to pick out high discriminative features. The directed acyclic graph support vector machine is finally used to classify infant crying. Experimental results have revealed the good performance of the proposed system and the classification accuracy is up to 92.17 %.

DAG-SVM based infant cry classification system using sequential forward floating feature selection

In this paper, we report on a free-hand motion capture study in which 32 participants ‘traced’ 16 melodic vocal phrases with their hands in the air in two experimental conditions. Melodic contours are often thought of as correlated with vertical movement (up and down) in time, and this was also our initial expectation. We did find an arch shape for most of the tracings, although this did not correspond directly to the melodic contours. Furthermore, representation of pitch in the vertical dimension was but one of a diverse range of movement strategies used to trace the melodies. Six different mapping strategies were observed, and these strategies have been quantified and statistically tested. The conclusion is that metaphorical representation is much more common than a ‘graph-like’ rendering for such a melodic sound-tracing task. Other findings include a clear gender difference for some of the tracing strategies and an unexpected representation of melodies in terms of a small object for some of the Hindustani music examples. The data also show a tendency of participants moving within a shared ‘social box’.

https://www.mdpi.com/2076-3417/8/1/135/pdf

Analyzing Free-Hand Sound-Tracings of Melodic Phrases

Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis

The detection of pathologies in the early stages of a baby's life has been one of the major challenges to overcome for the medical sciences. Lack of means of interpretation of this normal physical manifestation of the child has made this task extremely complicated. The discovery that the crying wave, as the sole initial means of communication of babies, contains information about their neurophysiological state, has opened the possibility of interpreting that state and to diagnose diseases from a few days of birth. In this paper we present the efforts to develop a practical integral system to automatically identify pathologies in newborn babies by selecting quantitative features and which also highlights different types of qualitative characteristics on the newborn infant crying through appropriate acoustic processes. And, in each case, after the features are selected or identified uses them to recognize the inherent pathology. Once the system is complete and fully tested we pretend to offer rural nurses, general doctors, researchers and scholars a tool as a mean to make noninvasive diagnostics and as an information support to allow them to have a solid perspective on relevant crying events, and to facilitate the development and unification of standards for the assessment or comprehensive description of the crying wave. Keywords— analysis of infant crying, automatic identification of qualitative characteristics, classification of crying, non-invasive diagnosis.

/pdf/on-the-development-of-a-non-invasive-pathologies-elsbvw76jm.pdf

On the Development of a Non-Invasive Pathologies Identification System by Qualitative and Quantitative Characterization of Infant Crying and the Application of Intelligent Classification Models to be Used in Rural Environments.

María Antonia Ruíz Díaz

Papers

Automatic infant cry analysis for the identification of qualitative features to help opportune diagnosis

On the Development of a Non-Invasive Pathologies Identification System by Qualitative and Quantitative Characterization of Infant Crying and the Application of Intelligent Classification Models to be Used in Rural Environments.