Estimation of the Lung Function Using Acoustic Features of the Voluntary Cough

Nemati, Eghlim; Rahman, Md. Juber; Blackstock, Erin; Nathan, Viswam; Rahman, Md. Mahbubur; Vatanparvar, Korosh; Kuang, Jilong

doi:10.1109/embc44109.2020.9175986

Cited by 16 publications

(13 citation statements)

References 18 publications

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“…Furthermore, in the bronchial asthma group, the power of cough sound was shifted to a higher frequency range compared with the control group (Knocikova et al, 2008). Another study (Nemati, Rahman, Blackstock, et al, 2020) found that cough duration, MFCC1 (Mel-frequency cepstral coefficient), and MFCC9 features were the most important acoustic features for classification of pulmonary disease state (i. e., bronchial asthma, COPD, chronic cough, healthy) and disease severity, defined based on a patient’s forced expiratory volume in the first second (FEV1) divided through the forced vital capacity (FVC). Similar to the speech/voice domain, various automatic approaches have proved to be effective at detecting pulmonary diseases from cough sounds (Infante, Chamberlain, Kodgule, et al, 2017; Nemati, Rahman, Blackstock, et al, 2020); good performance was even achieved when differentiating between two obstructive pulmonary diseases, namely bronchial asthma and COPD (Infante, Chamberlain, Fletcher, et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Another study (Nemati, Rahman, Blackstock, et al, 2020) found that cough duration, MFCC1 (Mel-frequency cepstral coefficient), and MFCC9 features were the most important acoustic features for classification of pulmonary disease state (i. e., bronchial asthma, COPD, chronic cough, healthy) and disease severity, defined based on a patient’s forced expiratory volume in the first second (FEV1) divided through the forced vital capacity (FVC). Similar to the speech/voice domain, various automatic approaches have proved to be effective at detecting pulmonary diseases from cough sounds (Infante, Chamberlain, Kodgule, et al, 2017; Nemati, Rahman, Blackstock, et al, 2020); good performance was even achieved when differentiating between two obstructive pulmonary diseases, namely bronchial asthma and COPD (Infante, Chamberlain, Fletcher, et al, 2017). Furthermore, using acoustic features extracted from cough sounds, the study (Nemati, Rahman, Blackstock, et al, 2020) automatically classified the symptom severity of patients with pulmonary diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to the speech/voice domain, various automatic approaches have proved to be effective at detecting pulmonary diseases from cough sounds (Infante, Chamberlain, Kodgule, et al, 2017; Nemati, Rahman, Blackstock, et al, 2020); good performance was even achieved when differentiating between two obstructive pulmonary diseases, namely bronchial asthma and COPD (Infante, Chamberlain, Fletcher, et al, 2017). Furthermore, using acoustic features extracted from cough sounds, the study (Nemati, Rahman, Blackstock, et al, 2020) automatically classified the symptom severity of patients with pulmonary diseases. In another study (Sharan et al, 2018), cough sound analysis was used to predict spirometry results, i. e., FVC, FEV1, and FEV1/FVC, for patients with obstructive, restrictive, and combined obstructive-restrictive pulmonary diseases as well as healthy controls.…”

Section: Introductionmentioning

confidence: 99%

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The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

Ren

Chang

Bartl-Pokorny

et al. 2022

Preprint

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Purpose: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. Methods: With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. Results: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

Ren

Chang

Bartl-Pokorny

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…46 Another study 47 found that cough duration, MFCC1 (Mel-frequency cepstral coefficient), and MFCC9 features were the most important acoustic features for classification of pulmonary disease state (i. e., bronchial asthma, COPD, chronic cough, healthy) and disease severity, defined based on a patient's forced expiratory volume in the first second (FEV1) divided through the forced vital capacity (FVC). Similar to the speech/voice domain, various automatic approaches have proved to be effective at detecting pulmonary diseases from cough sounds 47 , 48 ; good performance was even achieved when differentiating between two obstructive pulmonary diseases, namely bronchial asthma and COPD. 49 Furthermore, using acoustic features extracted from cough sounds, Nemati and colleagues 47 automatically classified the symptom severity of patients with pulmonary diseases.…”

Section: Introductionmentioning

confidence: 99%

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

et al. 2022

View full text Add to dashboard Cite

“…lung diseases yield staggering healthcare costs, bringing a huge economic and social burden to patients and the society. Since cough is an important indicator of respiratoryrelated disease, reliable detection of cough using wearables is especially desirable in healthcare community among doctors and clinical practitioners [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection

Zhang¹,

Nemati²,

Ahmed³

et al. 2021

Preprint

Self Cite

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Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves outstanding performance. However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed. But the accuracy is comprised of only one centroid obtained for each class. Centroid-based Classifier performs clustering and averaging for each cluster, but requires manually setting the number of clusters. We propose a novel self-tuning multi-centroid template-matching algorithm, which can automatically adjust the number of clusters to balance accuracy and inference time. Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance-Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.

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Estimation of the Lung Function Using Acoustic Features of the Voluntary Cough

Cited by 16 publications

References 18 publications

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection

A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection

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