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

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative). Samples were clinically labelled according to the results and severity based on quantitative RT-PCR (qRT-PCR) analysis, cycle threshold and lymphocytes count from the patients. Our proposed generic method is a algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features and deep artificial neural network classifier with convolutional layers called DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform proof-of-concept Web App CoughDetect to administer this test anonymously. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.800.83%, sensitivity of 96.431.85%, and specificity of 96.201.74%, and 81.08%5.05% AUC for the recognition of three severity levels. Our proposed web tool and underpinning algorithm for the robust, fast, point-of-need identification of Covid-19 facilitates the rapid detection of the infection. We believe that it has the potential to significantly hamper the Covid-19 pandemic across the world

Classifying infant cry patterns by the Genetic Selection of a Fuzzy Model

Infant crying analysis is an important tool for identifying different pathologies at a very early stage of the life of a baby. Being able to perform this task with high accuracy is therefore important and required as a medical support system to assess a baby's health. In this research we propose an automatic classification model for infant crying for early disease detection. Our model mainly consists of two phases: (a) an acoustic features acquisition from the Mel Frequency Cepstral Coefficient and the Linear Predictive Coding from signal processing and (b) the selection/creation of an optimized fuzzy model through the Genetic Selection of a Fuzzy Model (GSFM) algorithm. GSFM searches for the best model by choosing a combination of a feature selection method, a type of fuzzy processing, a learning algorithm together with its associated parameters that best fit the input data. Our approach improves the predictive accuracy on the identification of the cause of crying and clearly helps to differentiate between normal and pathological cry. Experimental results show a significant accuracy improvement when using our optimized genetic selection method for most of the cases