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

Ponomarchuk, Alexander; Burenko, Ilya; Malkin, Elian; Nazarov, Ivan; Kokh, Vladimir; Avetisian, Manvel; Zhukov, Leonid

doi:10.1109/jstsp.2022.3142514

Cited by 33 publications

(32 citation statements)

References 48 publications

(64 reference statements)

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“…Ponomarchuk et al [33] proposed a machine learning method to detect COVID-19 based on breath, voice rapidly, and cough of open, private datasets with verified labels from hospitals and app data. They proposed a hybrid ensemble model that combines vggish and cochleagram, Mel spectrogram, and gradient boosting with patient information as feature values, lightweight CNN, and logistic regression.…”

Section: Lightweight Modelsmentioning

confidence: 99%

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

Choi

Lee

et al. 2022

IEEE Access

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As the number of deaths from respiratory diseases due to COVID-19 and infectious diseases increases, early diagnosis is necessary. In general, the diagnosis of diseases is based on imaging devices (e.g., computed tomography and magnetic resonance imaging) as well as the patient's underlying disease information. However, these examinations are time-consuming, incur considerable costs, and in a situation like the ongoing pandemic, face-to-face examinations are difficult to conduct. Therefore, we propose a lung disease classification model based on deep learning using non-contact auscultation. In this study, two respiratory specialists collected normal respiratory sounds and five types of abnormal sounds associated with lung disease, including those associated with four lung lesions in the left and right anterior chest and left and right posterior chest. For preprocessing and feature extraction, the noise was removed using three pass filters (low, band, and high), and respiratory sound features were extracted using the Log-Mel Spectrogram-Mel Frequency Cepstral Coefficient followed by feature stacking. Then, we propose a lung disease classification model of dense lightweight convolutional neural network-bidirectional gated recurrent unit skip connections using depthwise separable convolution based on the extracted respiratory sound information. The performance of the classification model was compared with both the baseline and the lightweight models. The results indicate that the proposed model achieves high performance and has an accuracy of 92.3%, sensitivity of 92.1%, specificity of 98.5%, and f1-score of 91.9%. Using the proposed model, we aim to contribute to the early detection of diseases during the COVID-19 pandemic.

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Section: Lightweight Modelsmentioning

confidence: 99%

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

Choi

Lee

et al. 2022

IEEE Access

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“…A head-to-head comparison of the results presented in Table 1 would be hasty and unwise as the evaluation metrics were not reported consistently. For instance, Chaudhari et al [ 17 ], Coppock et al [ 29 ], Ponomarchuk et al [ 38 ] and Nguyen et al [ 39 ] used only the AUC metric, while Lella and Pja [ 40 ] used accuracy only. Different evaluation metrics are used as follows:

where FP refers to false positive, FN refers to false negative, TP refers to true positive, and TN refers to true negative.…”

Section: Discussionmentioning

confidence: 99%

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

2022

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In the past two years, medical researchers and data scientists worldwide have focused their efforts on containing the pandemic of coronavirus disease 2019 (COVID-19). Deep learning models have been proven to be capable of efficient medical diagnosis and prognosis in cancer, common lung diseases, and COVID-19. On the other hand, artificial neural networks have demonstrated their potential in pattern recognition and classification in various domains, including healthcare. This literature review aims to report the state of research on developing neural network models to diagnose COVID-19 from cough sounds to create a cost-efficient and accessible testing tool in the fight against the pandemic. A total of 35 papers were included in this review following a screening of the 161 outputs of the literature search. We extracted information from articles on data resources, model structures, and evaluation metrics and then explored the scope of experimental studies and methodologies and analyzed their outcomes and limitations. We found that cough is a biomarker, and its associated information can determine an individual’s health status. Convolutional neural networks were predominantly used, suggesting they are particularly suitable for feature extraction and classification. The reported accuracy values ranged from 73.1% to 98.5%. Moreover, the dataset sizes ranged from 16 to over 30,000 cough audio samples. Although deep learning is a promising prospect in identifying COVID-19, we identified a gap in the literature on research conducted over large and diversified data sets.

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“…We use a publicly available dataset of COVID and non-COVID cough recordings 20 . The data contain 1322 cough recordings from as many subjects, collected by the MedInGroup primary health network.…”

Section: Methodsmentioning

confidence: 99%

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

Xiong

Berkovsky

Kâafar

et al. 2022

Sci Rep

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Mass community testing is a critical means for monitoring the spread of the COVID-19 pandemic. Polymerase chain reaction (PCR) is the gold standard for detecting the causative coronavirus 2 (SARS-CoV-2) but the test is invasive, test centers may not be readily available, and the wait for laboratory results can take several days. Various machine learning based alternatives to PCR screening for SARS-CoV-2 have been proposed, including cough sound analysis. Cough classification models appear to be a robust means to predict infective status, but collecting reliable PCR confirmed data for their development is challenging and recent work using unverified crowdsourced data is seen as a viable alternative. In this study, we report experiments that assess cough classification models trained (i) using data from PCR-confirmed COVID subjects and (ii) using data of individuals self-reporting their infective status. We compare performance using PCR-confirmed data. Models trained on PCR-confirmed data perform better than those trained on patient-reported data. Models using PCR-confirmed data also exploit more stable predictive features and converge faster. Crowd-sourced cough data is less reliable than PCR-confirmed data for developing predictive models for COVID-19, and raises concerns about the utility of patient reported outcome data in developing other clinical predictive models when better gold-standard data are available.

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

Cited by 33 publications

References 48 publications

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review

Reliability of crowdsourced data and patient-reported outcome measures in cough-based COVID-19 screening

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