60 research outputs found
Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
The COVID-19 pandemic presents global challenges transcending boundaries of
country, race, religion, and economy. The current gold standard method for
COVID-19 detection is the reverse transcription polymerase chain reaction
(RT-PCR) testing. However, this method is expensive, time-consuming, and
violates social distancing. Also, as the pandemic is expected to stay for a
while, there is a need for an alternate diagnosis tool which overcomes these
limitations, and is deployable at a large scale. The prominent symptoms of
COVID-19 include cough and breathing difficulties. We foresee that respiratory
sounds, when analyzed using machine learning techniques, can provide useful
insights, enabling the design of a diagnostic tool. Towards this, the paper
presents an early effort in creating (and analyzing) a database, called
Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound
samples are collected via worldwide crowdsourcing using a website application.
The curated dataset is released as open access. As the pandemic is evolving,
the data collection and analysis is a work in progress. We believe that
insights from analysis of Coswara can be effective in enabling sound based
technology solutions for point-of-care diagnosis of respiratory infection, and
in the near future this can help to diagnose COVID-19.Comment: A description of Coswara dataset to evaluate COVID-19 diagnosis using
respiratory sound
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
The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals
Harnessing the power of Artificial Intelligence (AI) and m-health towards
detecting new bio-markers indicative of the onset and progress of respiratory
abnormalities/conditions has greatly attracted the scientific and research
interest especially during COVID-19 pandemic. The smarty4covid dataset contains
audio signals of cough (4,676), regular breathing (4,665), deep breathing
(4,695) and voice (4,291) as recorded by means of mobile devices following a
crowd-sourcing approach. Other self reported information is also included (e.g.
COVID-19 virus tests), thus providing a comprehensive dataset for the
development of COVID-19 risk detection models. The smarty4covid dataset is
released in the form of a web-ontology language (OWL) knowledge base enabling
data consolidation from other relevant datasets, complex queries and reasoning.
It has been utilized towards the development of models able to: (i) extract
clinically informative respiratory indicators from regular breathing records,
and (ii) identify cough, breath and voice segments in crowd-sourced audio
recordings. A new framework utilizing the smarty4covid OWL knowledge base
towards generating counterfactual explanations in opaque AI-based COVID-19 risk
detection models is proposed and validated.Comment: Submitted for publication in Nature Scientific Dat
COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
We present a machine learning based COVID-19 cough classifier which can
discriminate COVID-19 positive coughs from both COVID-19 negative and healthy
coughs recorded on a smartphone. This type of screening is non-contact, easy to
apply, and can reduce the workload in testing centres as well as limit
transmission by recommending early self-isolation to those who have a cough
suggestive of COVID-19. The datasets used in this study include subjects from
all six continents and contain both forced and natural coughs, indicating that
the approach is widely applicable. The publicly available Coswara dataset
contains 92 COVID-19 positive and 1079 healthy subjects, while the second
smaller dataset was collected mostly in South Africa and contains 18 COVID-19
positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV
laboratory test. Both datasets indicate that COVID-19 positive coughs are
15\%-20\% shorter than non-COVID coughs. Dataset skew was addressed by applying
the synthetic minority oversampling technique (SMOTE). A leave--out
cross-validation scheme was used to train and evaluate seven machine learning
classifiers: LR, KNN, SVM, MLP, CNN, LSTM and Resnet50. Our results show that
although all classifiers were able to identify COVID-19 coughs, the best
performance was exhibited by the Resnet50 classifier, which was best able to
discriminate between the COVID-19 positive and the healthy coughs with an area
under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to
discriminate between the COVID-19 positive and COVID-19 negative coughs, with
an AUC of 0.94 after selecting the best 13 features from a sequential forward
selection (SFS). Since this type of cough audio classification is
cost-effective and easy to deploy, it is potentially a useful and viable means
of non-contact COVID-19 screening.Comment: This paper has been accepted in "Computers in Medicine and Biology"
and currently under productio
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
Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification.
In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses
Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses
As the burden of respiratory diseases continues to fall on society worldwide,
this paper proposes a high-quality and reliable dataset of human sounds for
studying respiratory illnesses, including pneumonia and COVID-19. It consists
of coughing, mouth breathing, and nose breathing sounds together with metadata
on related clinical characteristics. We also develop a proof-of-concept system
for establishing baselines and benchmarking against multiple datasets, such as
Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr
dataset has richer features, better performance, and is more robust to dataset
shifts in various machine learning tasks. It is promising for a wide range of
real-time applications on mobile devices. The proposed dataset and system will
serve as practical tools to support healthcare professionals in diagnosing
respiratory disorders. The dataset and code are publicly available here:
https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH
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
COVID-19 activity screening by a smart-data-driven multi-band voice analysis
COVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance.info:eu-repo/semantics/acceptedVersio
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