A novel event-related fMRI supervoxels-based representation and its application to schizophrenia diagnosis.

Background And Objective: The schizophrenia diagnosis represents a difficult task because of the confusing descriptions of symptoms given by the patient, their similarity among several disorders, the lower familiarity with genetic predisposition, and the probably inadequate response to the treatment. Neuro-biological markers of schizophrenia, as a quantitative relationship between the psychiatrist's reports and the biology of the brain, could be used. Functional Magnetic Resonance Imaging (fMRI) obtain the subject's performance in cognitive tasks and may find significant differences between the patient's data and controls.

A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels.

In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers.

Analysis of facial expressions in parkinson's disease through video-based automatic methods.

Background: The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is the study of facial bradykinesia in Parkinson's disease (PD), which is a major motor sign of this neurodegenerative illness. Facial bradykinesia consists in the reduction/loss of facial movements and emotional facial expressions called hypomimia.

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

Objectives: Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques.

Spectrographic cry analysis in newborns with profound hearing loss and perinatal high-risk newborns.

Background: Crying is the first neurophysiological demonstration of the newborn. The acoustic analysis of crying episodes can provide useful information in the early diagnosis of several pathologies.

Methods: We carried out a spectrographic cry analysis of 40 infants with several diseases such as neonatal asphyxia, breathing disorders, deafness and neurological disorders.

[Cry analysis of hypoacoustic children and normal hearing children].

Objective: We analyzed the spectrograms of the cry of deaf children and compared the results with those of normal children.

Material And Methods: Twenty deaf children (0-2 years of age, both sexes) and 20 normal hearing children of both sexes within the same age range were studied. The deaf cases were selected from patients with high-risk pregnancies who are followed up at the National Institute of Perinatology (INPer), Department of Human Communication.