Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks.

This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodologies for the analysis of cough patterns in respiratory diseases. A cohort of 20 adult patients, all presenting persistent cough as a symptom of respiratory disease, was monitored for 24 hours using a smartphone. The audio signals underwent frequency domain transformation to yield 1-second spectrograms, subsequently processed by a CNN to detect cough events.

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups.

Unlabelled: Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in clinical assessment. Fortunately, recent advancements in artificial intelligence (AI) have shown promise in providing objective diagnoses through the analysis of medical images or activity recordings.

Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records.

Unlabelled: Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents. However, its etiology is still unknown, and this hinders the existence of reliable, fast and inexpensive standard diagnostic methods.

Objective: This paper proposes an end-to-end methodology for automatic diagnosis of the combined type of ADHD.