Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease pro- gression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from dig- ital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to under- stand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.ER

Analysis and Development of an End-to-End Convolutional Neural Network for Sounds Classification Through Deep Learning Techniques

El presente trabajo estudia el análisis y desarrollo continuo de un modelo de inteligencia artificial orientado a la clasificación de audio. El capítulo 1 presenta antecedentes sobre las diferentes tareas relacionadas a audio que la comunidad de investigación ha seguido a lo largo de los últimos años, también establece la hipótesis central de este trabajo y define objetivos generales y específicos para contribuir a la mejora del rendimiento sobre un generador de embeddings de audio de tipo end-to-end. El capítulo 2 presenta los métodos de vanguardia y trabajos publicados que se enfocan principalmente al desarrollo de la clasificación de audio y el aprendizaje profundo como disciplinas que aún tienen un gran potencial. El capítulo 3 presenta el marco conceptual en el que se basa esta tesis, dividido en dos secciones principales: preprocesamiento de audio y técnicas de aprendizaje profundo. Cada una de estas secciones se divide en varias subsecciones para representar el proceso de clasificación de audio a través de redes neuronales profundas. El capítulo 4 brinda una explicación profunda del generador de embeddings de audio llamado AemNet y sus componentes, utilizado como objeto de estudio, donde se detalla en las siguientes subsecciones. Se realizó una experimentación inicial sobre este enfoque y se presentaron resultados experimentales que sugirieron un mejor rendimiento mediante la modificación de las etapas de arquitectura de la red neuronal. El capítulo 5 es la primera aplicación objetivo de nuestra adaptación de AemNet que se presentó al desafío DCASE 2021. Los detalles sobre el desafío y los resultados se describen en las secciones de este capítulo, así como la metodología seguida para presentar nuestra propuesta. El capítulo 6 es la segunda aplicación objetivo y el primero en apuntar a los sonidos respiratorios. El desafío de ICBHI se explica en las secciones de este capítulo, así como la metodología y los experimentos realizados para llegar a un clasificador robusto que distingue cuatro anomalías de tos diferentes. Se creó un artículo a partir de la solución propuesta y se presentó en el IEEE LA-CCI 2021. El capítulo 7 aprovecha los diversos resultados anteriores para cumplir con un enfoque moderno como lo es la detección de COVID-19, cuya recopilación y experimentación de fuentes de datos se describen profundamente y los resultados experimentales sugieren que una adaptación de red residual denominada AemResNet, puede cumplir la función de distinguir a los pacientes con COVID-19 a partir de tos y sonidos respiratorios. Finalmente, las conclusiones de toda esta investigación y los resultados evaluados en cada una de las aplicaciones objetivo se discuten en el capítulo 8.ITESO, A. C

Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough

The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions. In this paper we propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection. The application showed robust performance on both open sourced datasets and on the noisy data collected during beta testing by the end users

Analyzing Cough Sounds for the Evidence of Covid-19 using Deep Learning Models

Early detection of infectious disease is the must to prevent/avoid multiple infections, and Covid-19 is an example. When dealing with Covid-19 pandemic, Cough is still ubiquitously presented as one of the key symptoms in both severe and non-severe Covid-19 infections, even though symptoms appear differently in different sociodemographic categories. By realizing the importance of clinical studies, analyzing cough sounds using AI-driven tools could help add more values when it comes to decision-making. Moreover, for mass screening and to serve resource constrained regions, AI-driven tools are the must. In this thesis, Convolutional Neural Network (CNN) tailored deep learning models are studied to analyze cough sounds to detect the possible evidence of Covid-19. In addition to custom CNN, pre-trained deep learning models (e.g., Vgg-16, Resnet-50, MobileNetV1, and DenseNet121) are employed on a publicly available dataset. In our findings, custom CNN performed comparatively better than pre-trained deep learning models

A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS

In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients

Robustness Assessment with a Data-Centric Machine Learning Pipeline

Publisher Copyright: AuthorAs long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound-based analysis of vocal and respiratory audio data to achieve that objective. This work presents a modular data-centric Machine Learning pipeline for COVID-19 identification from voice and respiratory audio samples. Signals are processed to extract and classify relevant segments that contain informative events, such as coughing or breathing. Temporal, amplitude, spectral, cepstral, and phonetic features are extracted from audio along with available metadata for COVID-19 identification. Audio augmentation and data balancing techniques are used to mitigate class disproportionality. The open-access Coswara and COVID-19 Sounds datasets were used to test the performance of the proposed architecture. Obtained sensitivity scores ranged from 60.00% to 80.00% in Coswara and from 51.43% to 77.14% in COVID-19 Sounds. Although previous works report higher accuracy on COVID-19 detection, this research focused on a data-centric approach by validating the quality of the samples, segmenting the speech events, and exploring interpretable features with physiological meaning. As the pandemic evolves, its lessons must endure, and pipelines such as the proposed one will help prepare new stages where quick and easy disease identification is essential.publishersversionepub_ahead_of_prin

A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification

Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the COUGHVID dataset. The COUGHVID dataset enlisted expert physicians to diagnose the underlying diseases present in a limited number of uploaded recordings. However, this approach suffers from potential mislabeling of the coughs, as well as notable disagreement between experts. In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification. First, we leverage existing SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classification models. The consistency of the re-labeled data is demonstrated in that it exhibits a high degree of class separability, 3x higher than that of the user-labeled data, despite the expert label inconsistency present in the original dataset. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs, which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a cough classifier. This SSL approach can be used to combine the medical knowledge of several experts to improve the database consistency for any diagnostic classification task

Sounds of COVID-19: exploring realistic performance of audio-based digital testing.

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools' performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65-0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85-0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment