1
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Xu W, Bao X, Lou X, Liu X, Chen Y, Zhao X, Zhang C, Pan C, Liu W, Liu F. Feature fusion method for pulmonary tuberculosis patient detection based on cough sound. PLoS One 2024; 19:e0302651. [PMID: 38743758 PMCID: PMC11093322 DOI: 10.1371/journal.pone.0302651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 04/08/2024] [Indexed: 05/16/2024] Open
Abstract
Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.
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Affiliation(s)
- Wenlong Xu
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Xiaofan Bao
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Xiaomin Lou
- Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, China
| | - Xiaofang Liu
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Chen
- Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, China
| | | | - Chenlu Zhang
- Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, China
| | - Chen Pan
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Wenlong Liu
- College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Queensland, Australia
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2
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Sharma M, Nduba V, Njagi LN, Murithi W, Mwongera Z, Hawn TR, Patel SN, Horne DJ. TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset. SCIENCE ADVANCES 2024; 10:eadi0282. [PMID: 38170773 PMCID: PMC10776005 DOI: 10.1126/sciadv.adi0282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
Recent respiratory disease screening studies suggest promising performance of cough classifiers, but potential biases in model training and dataset quality preclude robust conclusions. To examine tuberculosis (TB) cough diagnostic features, we enrolled subjects with pulmonary TB (N = 149) and controls with other respiratory illnesses (N = 46) in Nairobi. We collected a dataset with 33,000 passive coughs and 1600 forced coughs in a controlled setting with similar demographics. We trained a ResNet18-based cough classifier using images of passive cough scalogram as input and obtained a fivefold cross-validation sensitivity of 0.70 (±0.11 SD). The smartphone-based model had better performance in subjects with higher bacterial load {receiver operating characteristic-area under the curve (ROC-AUC): 0.87 [95% confidence interval (CI): 0.87 to 0.88], P < 0.001} or lung cavities [ROC-AUC: 0.89 (95% CI: 0.88 to 0.89), P < 0.001]. Overall, our data suggest that passive cough features distinguish TB from non-TB subjects and are associated with bacterial burden and disease severity.
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Affiliation(s)
- Manuja Sharma
- Department of Electrical and Computer Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA 98195, USA
| | - Videlis Nduba
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Mbagathi Rd, Nairobi 610101, Kenya
| | - Lilian N. Njagi
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Mbagathi Rd, Nairobi 610101, Kenya
| | - Wilfred Murithi
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Mbagathi Rd, Nairobi 610101, Kenya
| | - Zipporah Mwongera
- Centre for Respiratory Diseases Research, Kenya Medical Research Institute, Mbagathi Rd, Nairobi 610101, Kenya
| | - Thomas R. Hawn
- Department of Medicine, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Shwetak N. Patel
- Department of Electrical and Computer Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA 98195, USA
| | - David J. Horne
- Department of Medicine, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, USA
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3
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Carreiro-Martins P, Paixão P, Caires I, Rodrigues A, Matias P, Gamboa H, Carreiro A, Soares F, Gomez P, Sousa J, Neuparth N. Diagnosis of COVID-19 by sound-based analysis of vocal recordings. Pulmonology 2023; 29:455-456. [PMID: 37030999 PMCID: PMC10028339 DOI: 10.1016/j.pulmoe.2023.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/23/2023] Open
Affiliation(s)
- P Carreiro-Martins
- Comprehensive Health Research Center, NOVA Medical School, Campo Mártires da Pátria 130, 1169-056 Lisboa, Portugal; Serviço de Imunoalergologia, Hospital de Dona Estefânia, Centro Hospitalar Universitário de Lisboa Central, EPE, Rua Jacinta Marto, 1169-045 Lisboa, Portugal.
| | - P Paixão
- Comprehensive Health Research Center, NOVA Medical School, Campo Mártires da Pátria 130, 1169-056 Lisboa, Portugal
| | - I Caires
- Comprehensive Health Research Center, NOVA Medical School, Campo Mártires da Pátria 130, 1169-056 Lisboa, Portugal
| | - A Rodrigues
- Comprehensive Health Research Center, NOVA Medical School, Campo Mártires da Pátria 130, 1169-056 Lisboa, Portugal
| | - P Matias
- Fraunhofer Portugal AICOS - Porto, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
| | - H Gamboa
- Fraunhofer Portugal AICOS - Porto, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal; Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, Faculdade de Ciências e Tecnologia of NOVA, University of Lisbon, Portugal
| | - A Carreiro
- Fraunhofer Portugal AICOS - Porto, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
| | - F Soares
- Fraunhofer Portugal AICOS - Porto, Rua Alfredo Allen 455/461, 4200-135 Porto, Portugal
| | - P Gomez
- NeuSpeLab, CTB, Universidad Politécnica de Madrid, Campus de Montegancedo, s/n, 28223 Madrid, Spain
| | - J Sousa
- NOS Inovação, Rua Actor António Silva, 9 - 6º Piso, Campo Grande, 1600-404 Lisboa, Portugal
| | - N Neuparth
- Comprehensive Health Research Center, NOVA Medical School, Campo Mártires da Pátria 130, 1169-056 Lisboa, Portugal; Serviço de Imunoalergologia, Hospital de Dona Estefânia, Centro Hospitalar Universitário de Lisboa Central, EPE, Rua Jacinta Marto, 1169-045 Lisboa, Portugal
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4
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Reid M, Agbassi YJP, Arinaminpathy N, Bercasio A, Bhargava A, Bhargava M, Bloom A, Cattamanchi A, Chaisson R, Chin D, Churchyard G, Cox H, Denkinger CM, Ditiu L, Dowdy D, Dybul M, Fauci A, Fedaku E, Gidado M, Harrington M, Hauser J, Heitkamp P, Herbert N, Herna Sari A, Hopewell P, Kendall E, Khan A, Kim A, Koek I, Kondratyuk S, Krishnan N, Ku CC, Lessem E, McConnell EV, Nahid P, Oliver M, Pai M, Raviglione M, Ryckman T, Schäferhoff M, Silva S, Small P, Stallworthy G, Temesgen Z, van Weezenbeek K, Vassall A, Velásquez GE, Venkatesan N, Yamey G, Zimmerman A, Jamison D, Swaminathan S, Goosby E. Scientific advances and the end of tuberculosis: a report from the Lancet Commission on Tuberculosis. Lancet 2023; 402:1473-1498. [PMID: 37716363 DOI: 10.1016/s0140-6736(23)01379-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/14/2023] [Accepted: 06/29/2023] [Indexed: 09/18/2023]
Affiliation(s)
- Michael Reid
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA.
| | - Yvan Jean Patrick Agbassi
- Global TB Community Advisory Board, Abidjan, Côte d'Ivoire, Yenepoya Medical College, Mangalore, India
| | | | - Alyssa Bercasio
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Anurag Bhargava
- Department of General Medicine, Yenepoya Medical College, Mangalore, India
| | - Madhavi Bhargava
- Department of Community Medicine, Yenepoya Medical College, Mangalore, India
| | - Amy Bloom
- Division of Tuberculosis, Bureau of Global Health, USAID, Washington, DC, USA
| | | | - Richard Chaisson
- Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel Chin
- Bill and Melinda Gates Foundation, Seattle, WA, USA
| | | | - Helen Cox
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Claudia M Denkinger
- Heidelberg University Hospital, German Center of Infection Research, Heidelberg, Germany
| | | | - David Dowdy
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Mark Dybul
- Department of Medicine, Center for Global Health Practice and Impact, Georgetown University, Washington, DC, USA
| | - Anthony Fauci
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | - Petra Heitkamp
- McGill International TB Centre, McGill University, Montreal, QC, Canada
| | - Nick Herbert
- Global TB Caucus, Houses of Parliament, London, UK
| | | | - Philip Hopewell
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | - Emily Kendall
- Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Aamir Khan
- Interactive Research & Development, Karachi, Pakistan
| | - Andrew Kim
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | | | | | - Nalini Krishnan
- Resource Group for Education and Advocacy for Community Health (REACH), Chennai, India
| | - Chu-Chang Ku
- School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Erica Lessem
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | | | - Payam Nahid
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | | | - Madhukar Pai
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; McGill International TB Centre, McGill University, Montreal, QC, Canada
| | - Mario Raviglione
- Centre for Multidisciplinary Research in Health Science, University of Milan, Milan, Italy
| | - Theresa Ryckman
- Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | | | - Sachin Silva
- Harvard TH Chan School of Public Health, Harvard University, Cambridge, MA, USA
| | | | | | | | | | - Anna Vassall
- Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Gustavo E Velásquez
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | | | - Gavin Yamey
- Center for Policy Impact in Global Health, Duke Global Health Institute, Duke University, Durham, NC, USA
| | | | - Dean Jamison
- Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
| | | | - Eric Goosby
- University of California San Francisco Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA; Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA
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5
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Dohál M, Porvazník I, Solovič I, Mokrý J. Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine. Front Microbiol 2023; 14:1225438. [PMID: 37860132 PMCID: PMC10582268 DOI: 10.3389/fmicb.2023.1225438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/22/2023] [Indexed: 10/21/2023] Open
Abstract
Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.
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Affiliation(s)
- Matúš Dohál
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
| | - Igor Porvazník
- National Institute of Tuberculosis, Lung Diseases and Thoracic Surgery, Vyšné Hágy, Slovakia
- Faculty of Health, Catholic University in Ružomberok, Ružomberok, Slovakia
| | - Ivan Solovič
- National Institute of Tuberculosis, Lung Diseases and Thoracic Surgery, Vyšné Hágy, Slovakia
- Faculty of Health, Catholic University in Ružomberok, Ružomberok, Slovakia
| | - Juraj Mokrý
- Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
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6
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Triantafyllopoulos A, Kathan A, Baird A, Christ L, Gebhard A, Gerczuk M, Karas V, Hübner T, Jing X, Liu S, Mallol-Ragolta A, Milling M, Ottl S, Semertzidou A, Rajamani ST, Yan T, Yang Z, Dineley J, Amiriparian S, Bartl-Pokorny KD, Batliner A, Pokorny FB, Schuller BW. HEAR4Health: a blueprint for making computer audition a staple of modern healthcare. Front Digit Health 2023; 5:1196079. [PMID: 37767523 PMCID: PMC10520966 DOI: 10.3389/fdgth.2023.1196079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.
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Affiliation(s)
- Andreas Triantafyllopoulos
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Kathan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alice Baird
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Lukas Christ
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Alexander Gebhard
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Maurice Gerczuk
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Vincent Karas
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Tobias Hübner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Xin Jing
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shuo Liu
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Adria Mallol-Ragolta
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Manuel Milling
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Sandra Ottl
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Anastasia Semertzidou
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | | | - Tianhao Yan
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Zijiang Yang
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Judith Dineley
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Shahin Amiriparian
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Katrin D. Bartl-Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Anton Batliner
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B. Pokorny
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
| | - Björn W. Schuller
- EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Augsburg, Germany
- Centre for Interdisciplinary Health Research, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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7
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Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput Biol Med 2023; 163:107153. [PMID: 37321101 PMCID: PMC10249348 DOI: 10.1016/j.compbiomed.2023.107153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
This study proposes a new deep learning-based method that demonstrates high performance in detecting Covid-19 disease from cough, breath, and voice signals. This impressive method, named CovidCoughNet, consists of a deep feature extraction network (InceptionFireNet) and a prediction network (DeepConvNet). The InceptionFireNet architecture, based on Inception and Fire modules, was designed to extract important feature maps. The DeepConvNet architecture, which is made up of convolutional neural network blocks, was developed to predict the feature vectors obtained from the InceptionFireNet architecture. The COUGHVID dataset containing cough data and the Coswara dataset containing cough, breath, and voice signals were used as the data sets. The pitch-shifting technique was used to data augmentation the signal data, which significantly contributed to improving performance. Additionally, Chroma features (CF), Root mean square energy (RMSE), Spectral centroid (SC), Spectral bandwidth (SB), Spectral rolloff (SR), Zero crossing rate (ZCR), and Mel frequency cepstral coefficients (MFCC) feature extraction techniques were used to extract important features from voice signals. Experimental studies have shown that using the pitch-shifting technique improved performance by around 3% compared to raw signals. When the proposed model was used with the COUGHVID dataset (Healthy, Covid-19, and Symptomatic), a high performance of 99.19% accuracy, 0.99 precision, 0.98 recall, 0.98 F1-Score, 97.77% specificity, and 98.44% AUC was achieved. Similarly, when the voice data in the Coswara dataset was used, higher performance was achieved compared to the cough and breath studies, with 99.63% accuracy, 100% precision, 0.99 recall, 0.99 F1-Score, 99.24% specificity, and 99.24% AUC. Moreover, when compared with current studies in the literature, the proposed model was observed to exhibit highly successful performance. The codes and details of the experimental studies can be accessed from the relevant Github page: (https://github.com/GaffariCelik/CovidCoughNet).
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Affiliation(s)
- Gaffari Celik
- Agri Ibrahim Cecen University, Department of Computer Technology, Agri, Turkey.
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8
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Liyanarachchi R, Wijekoon J, Premathilaka M, Vidhanaarachchi S. COVID-19 symptom identification using Deep Learning and hardware emulated systems. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 125:106709. [PMID: 38620194 PMCID: PMC10300286 DOI: 10.1016/j.engappai.2023.106709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 05/27/2023] [Accepted: 06/21/2023] [Indexed: 04/17/2024]
Abstract
The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.
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9
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Utilizing CNN-LSTM techniques for the enhancement of medical systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 72:323-338. [PMCID: PMC10105249 DOI: 10.1016/j.aej.2023.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 04/04/2024]
Abstract
COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%.
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10
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Yellapu GD, Rudraraju G, Sripada NR, Mamidgi B, Jalukuru C, Firmal P, Yechuri V, Varanasi S, Peddireddi VS, Bhimarasetty DM, Kanisetti S, Joshi N, Mohapatra P, Pamarthi K. Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB. Sci Rep 2023; 13:4740. [PMID: 36959347 PMCID: PMC10034902 DOI: 10.1038/s41598-023-31772-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/16/2023] [Indexed: 03/25/2023] Open
Abstract
Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.
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Affiliation(s)
| | | | | | | | - Charan Jalukuru
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | - Priyanka Firmal
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | - Venkat Yechuri
- Salcit Technologies, Jayabheri Silicon Towers, Hyderabad, India
| | | | | | | | | | - Niranjan Joshi
- Centre for Cellular and Molecular Platforms, Bengaluru, India
| | - Prasant Mohapatra
- Department of Computer Science, University of California, Davis, USA
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11
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Zhang J, Wu J, Qiu Y, Song A, Li W, Li X, Liu Y. Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review. Comput Biol Med 2023; 153:106517. [PMID: 36623438 PMCID: PMC9814440 DOI: 10.1016/j.compbiomed.2022.106517] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/23/2022] [Accepted: 12/31/2022] [Indexed: 01/07/2023]
Abstract
The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.
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Affiliation(s)
- Jun Zhang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China,Corresponding author
| | - Jingyue Wu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Yiyi Qiu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Aiguo Song
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
| | - Weifeng Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Xin Li
- Department of Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yecheng Liu
- Emergency Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100730, China
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12
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Jonathan J, Barakabitze AA. ML technologies for diagnosing and treatment of tuberculosis: a survey. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00727-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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13
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Chetupalli SR, Krishnan P, Sharma N, Muguli A, Kumar R, Nanda V, Pinto LM, Ghosh PK, Ganapathy S. Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:199-210. [PMID: 36909300 PMCID: PMC9994626 DOI: 10.1109/jtehm.2023.3250700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 12/05/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023]
Abstract
BACKGROUND The COVID-19 pandemic has highlighted the need to invent alternative respiratory health diagnosis methodologies which provide improvement with respect to time, cost, physical distancing and detection performance. In this context, identifying acoustic bio-markers of respiratory diseases has received renewed interest. OBJECTIVE In this paper, we aim to design COVID-19 diagnostics based on analyzing the acoustics and symptoms data. Towards this, the data is composed of cough, breathing, and speech signals, and health symptoms record, collected using a web-application over a period of twenty months. METHODS We investigate the use of time-frequency features for acoustic signals and binary features for encoding different health symptoms. We experiment with use of classifiers like logistic regression, support vector machines and long-short term memory (LSTM) network models on the acoustic data, while decision tree models are proposed for the symptoms data. RESULTS We show that a multi-modal integration of inference from different acoustic signal categories and symptoms achieves an area-under-curve (AUC) of 96.3%, a statistically significant improvement when compared against any individual modality ([Formula: see text]). Experimentation with different feature representations suggests that the mel-spectrogram acoustic features performs relatively better across the three kinds of acoustic signals. Further, a score analysis with data recorded from newer SARS-CoV-2 variants highlights the generalization ability of the proposed diagnostic approach for COVID-19 detection. CONCLUSION The proposed method shows a promising direction for COVID-19 detection using a multi-modal dataset, while generalizing to new COVID variants.
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Affiliation(s)
- Srikanth Raj Chetupalli
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Prashant Krishnan
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Neeraj Sharma
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Ananya Muguli
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Rohit Kumar
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Viral Nanda
- P. D. Hinduja National Hospital and Medical Research Center Mumbai 400016 India
| | - Lancelot Mark Pinto
- P. D. Hinduja National Hospital and Medical Research Center Mumbai 400016 India
| | - Prasanta Kumar Ghosh
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
| | - Sriram Ganapathy
- LEAP LaboratoryDepartment of Electrical EngineeringIndian Institute of Science Bengaluru 560012 India
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14
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Hamidi M, Zealouk O, Satori H, Laaidi N, Salek A. COVID-19 assessment using HMM cough recognition system. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2023; 15:193-201. [PMID: 36313860 PMCID: PMC9595586 DOI: 10.1007/s41870-022-01120-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022]
Abstract
This paper is a part of our contributions to research on the ongoing COVID-19 pandemic around the world. This research aims to use Hidden Markov Model (HMM) based automatic speech recognition system to analyze the cough signal and determine whether the signal belongs to a sick or healthy speaker. We built a configurable model by using HMMs, Gaussian Mixture Models (GMMs), Mel frequency spectral coefficients (MFCCs) and a cough corpus collected from healthy and sick voluntary speakers. Our proposed method is able to classify dry cough with sensitivity from 85.86% to 91.57%, differentiate the dry cough, and cough COVID-19 symptom with specificity from 5 to 10%. The obtained results are very encouraging to enrich our corpus with more data and increase the performance of our diagnostic system.
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Affiliation(s)
- Mohamed Hamidi
- Advanced Systems Engineering Laboratory, ENSA-UIT, Kenitra, Morocco ,grid.412150.30000 0004 0648 5985Multimedia and Arts Department, FLLA, UIT, Kenitra, Morocco
| | - Ouissam Zealouk
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Hassan Satori
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Naouar Laaidi
- LISAC, Department of Mathematics and Computer Science, FSDM, USMBA, Fez, Morocco
| | - Amine Salek
- Faculty of Medicine and Pharmacy, UMP, Oujda, Morocco
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15
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Cohen-McFarlane M, Xi P, Wallace B, Habashy K, Huq S, Goubran R, Knoefel F. Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:134-141. [PMID: 36578775 PMCID: PMC9788675 DOI: 10.1109/ojemb.2022.3202435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/06/2022] [Accepted: 08/25/2022] [Indexed: 12/31/2022] Open
Abstract
Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three classification tasks (C1: wet vs. dry vs. whooping cough vs. restricted breathing; C2: wet vs. dry cough; C3: cough vs. restricted breathing). Methods: The five visualizations (linear spectrogram, logarithmic spectrogram, Mel-spectrogram, wavelet scalograms, and aggregate images) are applied to a pre-trained AlexNet image classifier for all tasks. Results: The aggregate image-based classifier achieved the highest overall performance across all tasks with C1, C2, and C3 having testing accuracies of 0.88, 0.88, and 0.91 respectively. However, the Mel-spectrogram method had the highest testing accuracy (0.94) for C2. Conclusions: The classification of respiratory events using aggregate image inputs to transfer learning approaches may help healthcare professionals by providing information that would otherwise be unavailable to them.
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Affiliation(s)
- Madison Cohen-McFarlane
- AGE-WELL NCECarleton University Ottawa ON K1S 5B6 Canada
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
| | - Pengcheng Xi
- Digital Technologies Research CentreNational Research Council Canada Ottawa ON K1A 0R6 Canada
| | - Bruce Wallace
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
- AGE-WELL NCECarleton University Ottawa ON K1S 5B7 Canada
- Bruyère Research Institute Ottawa ON K1N 5C8 Canada
| | - Karim Habashy
- National Research Council Canada Ottawa ON K1A 0R6 Canada
| | - Saiful Huq
- Department of Systems and Computer Engineering, Carleton University Ottawa ON K1S 5B6 Canada
| | - Rafik Goubran
- AGE-WELL SAM3 National Innovation HubCarleton University Ottawa ON K1S 5B7 Canada
- Bruyère Research Institute Ottawa ON K1N 5C8 Canada
| | - Frank Knoefel
- Bruyère Research Institute, Bruyère Continuing CareElisabeth Bruyère Hospital Ottawa ON K1N 5C8 Canada
- AGE-WELL NCECarleton University Ottawa ON K1S 5B6 Canada
- AGE-WELL SAM3 National Innovation Hub Ottawa ON K1S 5B7 Canada
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16
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Pahar M, Miranda I, Diacon A, Niesler T. Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals. JOURNAL OF SIGNAL PROCESSING SYSTEMS 2022; 94:821-835. [PMID: 35341095 PMCID: PMC8934184 DOI: 10.1007/s11265-022-01748-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 01/09/2022] [Accepted: 02/23/2022] [Indexed: 12/01/2022]
Abstract
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient’s bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients. Logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) classifiers provide a baseline and are compared with three deep architectures, convolutional neural network (CNN), long short-term memory (LSTM) network, and residual-based architecture (Resnet50) using a leave-one-out cross-validation scheme. We find that it is possible to use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance, achieving an area under the ROC curve (AUC) exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.
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Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7600 Western Cape South Africa
| | - Igor Miranda
- Federal University of Recôncavo da Bahia, Cruz das Almas, 44.380-000 Bahia Brazil
| | - Andreas Diacon
- TASK Applied Science, Cape Town, Western Cape South Africa
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, 7600 Western Cape South Africa
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17
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Zimmer AJ, Ugarte-Gil C, Pathri R, Dewan P, Jaganath D, Cattamanchi A, Pai M, Grandjean Lapierre S. Making cough count in tuberculosis care. COMMUNICATIONS MEDICINE 2022; 2:83. [PMID: 35814294 PMCID: PMC9258463 DOI: 10.1038/s43856-022-00149-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 06/21/2022] [Indexed: 11/28/2022] Open
Abstract
Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade.
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Affiliation(s)
- Alexandra J. Zimmer
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- McGill International TB Centre, Montreal, Canada
| | - César Ugarte-Gil
- School of Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru
- Instituto de Medicina Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Puneet Dewan
- Bill & Melinda Gates Foundation, Seattle, WA USA
| | - Devan Jaganath
- Department of Medicine, Division of Pulmonary & Critical Care Medicine, University of California, San Francisco, 1001 Potrero Avenue, San Francisco, CA 94110 USA
- Center for Tuberculosis, University of California, San Francisco, 1001 Potrero Avenue, San Francisco, CA 94110 USA
| | - Adithya Cattamanchi
- Department of Medicine, Division of Pulmonary & Critical Care Medicine, University of California, San Francisco, 1001 Potrero Avenue, San Francisco, CA 94110 USA
- Center for Tuberculosis, University of California, San Francisco, 1001 Potrero Avenue, San Francisco, CA 94110 USA
| | - Madhukar Pai
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- McGill International TB Centre, Montreal, Canada
| | - Simon Grandjean Lapierre
- McGill International TB Centre, Montreal, Canada
- Immunopathology Axis, Centre de Recherche du Centre Hospitalier de l’Université de Montréal, 900 Rue Saint-Denis, Montréal, QC Canada
- Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, 2900 Boulevard Edouard-Montpetit, Montréal, QC Canada
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18
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Abdulgader SM, Okunola AO, Ndlangalavu G, Reeve BWP, Allwood BW, Koegelenberg CFN, Warren RM, Theron G. Diagnosing Tuberculosis: What Do New Technologies Allow Us to (Not) Do? Respiration 2022; 101:797-813. [PMID: 35760050 PMCID: PMC9533455 DOI: 10.1159/000525142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/10/2022] [Indexed: 12/11/2022] Open
Abstract
New tuberculosis (TB) diagnostics are at a crossroads: their development, evaluation, and implementation is severely damaged by resource diversion due to COVID-19. Yet several technologies, especially those with potential for non-invasive non-sputum-based testing, hold promise for efficiently triaging and rapidly confirming TB near point-of-care. Such tests are, however, progressing through the pipeline slowly and will take years to reach patients and health workers. Compellingly, such tests will create new opportunities for difficult-to-diagnose populations, including primary care attendees (all-comers in high burden settings irrespective of reason for presentation) and community members (with early stage disease or risk factors like HIV), many of whom cannot easily produce sputum. Critically, all upcoming technologies have limitations that implementers and health workers need to be cognizant of to ensure optimal deployment without undermining confidence in a technology that still offers improvements over the status quo. In this state-of-the-art review, we critically appraise such technologies for active pulmonary TB diagnosis. We highlight strengths, limitations, outstanding research questions, and how current and future tests could be used in the presence of these limitations and uncertainties. Among triage tests, CRP (for which commercial near point-of-care devices exist) and computer-aided detection software with digital chest X-ray hold promise, together with late-stage blood-based assays that detect host and/or microbial biomarkers; however, aside from a handful of prototypes, the latter category has a shortage of promising late-stage alternatives. Furthermore, positive results from new triage tests may have utility in people without TB; however, their utility for informing diagnostic pathways for other diseases is under-researched (most sick people tested for TB do not have TB). For confirmatory tests, few true point-of-care options will be available soon; however, combining novel approaches like tongue swabs with established tests like Ultra have short-term promise but first require optimizations to specimen collection and processing procedures. Concerningly, no technologies yet have compelling evidence of meeting the World Health Organization optimal target product profile performance criteria, especially for important operational criteria crucial for field deployment. This is alarming as the target product profile criteria are themselves almost a decade old and require urgent revision, especially to cater for technologies made prominent by the COVID-19 diagnostic response (e.g., at-home testing and connectivity solutions). Throughout the review, we underscore the importance of how target populations and settings affect test performance and how the criteria by which these tests should be judged vary by use case, including in active case finding. Lastly, we advocate for health workers and researchers to themselves be vocal proponents of the uptake of both new tests and those − already available tests that remain suboptimally utilized.
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Affiliation(s)
- Shima M Abdulgader
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Anna O Okunola
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gcobisa Ndlangalavu
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Byron W P Reeve
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Brian W Allwood
- Division of Pulmonology, Department of Medicine, Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - Coenraad F N Koegelenberg
- Division of Pulmonology, Department of Medicine, Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - Rob M Warren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Grant Theron
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
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Gupta S, Chakraborty S, Thakur CS. Neuromorphic Time-Multiplexed Reservoir Computing With On-the-Fly Weight Generation for Edge Devices. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2676-2685. [PMID: 34125686 DOI: 10.1109/tnnls.2021.3085165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The human brain has evolved to perform complex and computationally expensive cognitive tasks, such as audio-visual perception and object detection, with ease. For instance, the brain can recognize speech in different dialects and perform other cognitive tasks, such as attention, memory, and motor control, with just 20 W of power consumption. Taking inspiration from neural systems, we propose a low-power neuromorphic hardware architecture to perform classification on temporal data at the edge. The proposed architecture uses a neuromorphic cochlea model for feature extraction and reservoir computing (RC) framework as a classifier. In the proposed hardware architecture, the RC framework is modified for on-the-fly generation of reservoir connectivity, along with binary feedforward and reservoir weights. Also, a large reservoir is split into multiple small reservoirs for efficient use of hardware resources. These modifications reduce the computational and memory resources required, thereby resulting in a lower power budget. The proposed classifier is validated for speech and human activity recognition (HAR) tasks. We have prototyped our hardware architecture using Intel's cyclone-10 low-power series field-programmable gate array (FPGA), consuming only 4790 logic elements (LEs) and 34.9-kB memory, making it a perfect candidate for edge computing applications. Moreover, we have implemented a complete system for speech recognition with the feature extraction block (cochlea model) and the proposed classifier, utilizing 15 532 LEs and 38.4-kB memory. By using the proposed idea of multiple small reservoirs along with on-the-fly generation of reservoir binary weights, our architecture can reduce the power consumption and memory requirement by order of magnitude compared to existing FPGA models for speech recognition tasks with similar complexity.
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20
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Chowdhury NK, Kabir MA, Rahman MM, Islam SMS. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput Biol Med 2022; 145:105405. [PMID: 35318171 PMCID: PMC8926945 DOI: 10.1016/j.compbiomed.2022.105405] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022]
Abstract
This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97).
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Affiliation(s)
- Nihad Karim Chowdhury
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh,Corresponding author
| | - Muhammad Ashad Kabir
- Data Science Research Unit, School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia
| | - Md. Muhtadir Rahman
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh
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21
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Serrurier A, Neuschaefer-Rube C, Röhrig R. Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:2896. [PMID: 35458885 PMCID: PMC9027375 DOI: 10.3390/s22082896] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 11/16/2022]
Abstract
Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.
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Affiliation(s)
- Antoine Serrurier
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Christiane Neuschaefer-Rube
- Clinic for Phoniatrics, Pedaudiology & Communication Disorders, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
| | - Rainer Röhrig
- Institute of Medical Informatics, University Hospital of the RWTH Aachen, 52057 Aachen, Germany;
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22
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Nathavitharana RR, Garcia-Basteiro AL, Ruhwald M, Cobelens F, Theron G. Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all? EBioMedicine 2022; 78:103939. [PMID: 35339423 PMCID: PMC9043971 DOI: 10.1016/j.ebiom.2022.103939] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/14/2022] [Accepted: 02/28/2022] [Indexed: 01/26/2023] Open
Abstract
Rapid, accurate, sputum-free tests for tuberculosis (TB) triage and confirmation are urgently needed to close the widening diagnostic gap. We summarise key technologies and review programmatic, systems, and resource issues that could affect the impact of diagnostics. Mid-to-early-stage technologies like artificial intelligence-based automated digital chest X-radiography and capillary blood point-of-care assays are particularly promising. Pitfalls in the diagnostic pipeline, included a lack of community-based tools. We outline how these technologies may complement one another within the context of the TB care cascade, help overturn current paradigms (eg, reducing syndromic triage reliance, permitting subclinical TB to be diagnosed), and expand options for extra-pulmonary TB. We review challenges such as the difficulty of detecting paucibacillary TB and the limitations of current reference standards, and discuss how researchers and developers can better design and evaluate assays to optimise programmatic uptake. Finally, we outline how leveraging the urgency and innovation applied to COVID-19 is critical to improving TB patients' diagnostic quality-of-care.
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Affiliation(s)
- Ruvandhi R Nathavitharana
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, USA
| | - Alberto L Garcia-Basteiro
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain; Centro de Investigação em Saude de Manhiça, Maputo, Mozambique
| | - Morten Ruhwald
- FIND, the global alliance for diagnostics, Geneva, Switzerland
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Grant Theron
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa.
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23
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Ijaz A, Nabeel M, Masood U, Mahmood T, Hashmi MS, Posokhova I, Rizwan A, Imran A. Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2021.100832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
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24
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Zürcher K, Riou J, Morrow C, Ballif M, Koch A, Bertschinger S, Warner DF, Middelkoop K, Wood R, Egger M, Fenner L. Estimating Tuberculosis Transmission Risks in a Primary Care Clinic in South Africa: Modeling of Environmental and Clinical Data. J Infect Dis 2022; 225:1642-1652. [PMID: 35039860 PMCID: PMC9071349 DOI: 10.1093/infdis/jiab534] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 11/09/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Congregate settings, such as healthcare clinics, may play an essential role in Mycobacterium tuberculosis (Mtb) transmission. Using patient and environmental data, we studied transmission at a primary care clinic in South Africa. METHODS We collected patient movements, cough frequency, and clinical data, and measured indoor carbon dioxide (CO2) levels, relative humidity, and Mtb genomes in the air. We used negative binomial regression model to investigate associations. RESULTS We analyzed 978 unique patients who contributed 14 795 data points. The median patient age was 33 (interquartile range [IQR], 26-41) years, and 757 (77.4%) were female. Overall, median CO2 levels were 564 (IQR 495-646) parts per million and were highest in the morning. Median number of coughs per day was 466 (IQR, 368-503), and overall median Mtb DNA copies/μL/day was 4.2 (IQR, 1.2-9.5). We found an increased presence of Mtb DNA in the air of 32% (95% credible interval, 7%-63%) per 100 additional young adults (aged 15-29 years) and 1% (0-2%) more Mtb DNA per 10% increase of relative humidity. Estimated cumulative transmission risks for patients attending the clinic monthly for at least 1 hour range between 9% and 29%. CONCLUSIONS We identified young adults and relative humidity as potentially important factors for transmission risks in healthcare clinics. Our approach should be used to detect transmission and evaluate infection control interventions.
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Affiliation(s)
- Kathrin Zürcher
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Carl Morrow
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa,Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, University of Cape Town, Cape Town, South Africa
| | - Marie Ballif
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Anastasia Koch
- South African Medical Research Council/National Health Laboratory Service/University of Cape Town Molecular Mycobacteriology Research Unit and Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical TB Research, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Simon Bertschinger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Institute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland
| | - Digby F Warner
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa,South African Medical Research Council/National Health Laboratory Service/University of Cape Town Molecular Mycobacteriology Research Unit and Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical TB Research, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Keren Middelkoop
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa,Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, University of Cape Town, Cape Town, South Africa
| | - Robin Wood
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa,Desmond Tutu HIV Centre, Department of Medicine, University of Cape Town, University of Cape Town, Cape Town, South Africa
| | - Matthias Egger
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa,Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Lukas Fenner
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Correspondence: Lukas Fenner, MD, MSc, Institute of Social and Preventive Medicine, University of Bern (ISPM), Mittelstrasse 43, 3012 Bern, Switzerland ()
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25
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Keen EM, True EJ, Summers AR, Smith EC, Brew J, Grandjean Lapierre S. High-throughput digital cough recording on a university campus: A SARS-CoV-2-negative curated open database and operational template for acoustic screening of respiratory diseases. Digit Health 2022; 8:20552076221097513. [PMID: 35558638 PMCID: PMC9087241 DOI: 10.1177/20552076221097513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Respiratory illnesses have information-rich acoustic biomarkers, such as cough, that
can potentially play an important role in screening populations for disease risk. To
realize that potential, datasets of paired acoustic-clinical samples are needed for the
development and validation of acoustic screening models, and protocols for collecting
acoustic samples must be efficient and safe. We collected cough acoustic signatures at a
high-throughput SARS-CoV-2 testing site on a college campus. Here, we share logistical
details and the dataset of acoustic cough signatures paired with the gold standard in
SARS-CoV-2 testing of SARS-CoV-2 genomic sequences using qRT-PCR. Methods Cough recordings were collected in winter-spring 2021 at a rural residential college
(Sewanee, TN, USA), where approximately 2000 students were tested for SARS-CoV-2 on a
weekly basis. Cough collection was managed by student volunteers using custom
software. Results 4302 coughs were recorded from 960 participants over 11 weeks. All coughs were COVID-19
negative. Approximately 30 s were required to check-in a participant and collect their
cough. Conclusion The value of acoustic screening tools depends upon our ability to develop and implement
them reliably and quickly. For that to happen, high-quality datasets and logistical
insights must be collected and shared on an ongoing basis.
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Affiliation(s)
- Eric M. Keen
- Sewanee: The University of the South, Sewanee, TN, USA
- Hyfe, Inc., Wilmington, DE, USA
| | - Emily J. True
- Sewanee: The University of the South, Sewanee, TN, USA
| | | | | | | | - Simon Grandjean Lapierre
- Department of Microbiology, Infectious Diseases and Immunology, Université de Montréal, Montréal, Québec, Canada
- Immunopathology Axis, Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montréal, Québec, Canada
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COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features. Comput Biol Med 2021; 141:105153. [PMID: 34954610 PMCID: PMC8679499 DOI: 10.1016/j.compbiomed.2021.105153] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 12/15/2022]
Abstract
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes: coughs, breaths and speech. This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improved performance, but also to a marked reduction in the standard deviation of the classifier AUCs measured over the outer folds during nested cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic COVID-19 detection with a better and more consistent overall performance.
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Perez-Espinosa H, Timonet-Andreu E, Andreu-Perez J. Bias and privacy in AI's cough-based COVID-19 recognition. Lancet Digit Health 2021; 3:e760. [PMID: 34823703 PMCID: PMC8608398 DOI: 10.1016/s2589-7500(21)00234-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/05/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Humberto Perez-Espinosa
- Faculty of Science and Health, Centre for Computational Intelligence, University of Essex, Colchester CO4 3SQ, UK; National Council of Science and Technology, CICESE-UT3, Research Transfer Unit, Ensenada, Mexico
| | - Eva Timonet-Andreu
- Costa del Sol Health Agency, Biomedical Research Institute of Malaga, Malaga, Spain
| | - Javier Andreu-Perez
- Faculty of Science and Health, Centre for Computational Intelligence, University of Essex, Colchester CO4 3SQ, UK.
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Sharma NK, Muguli A, Krishnan P, Kumar R, Chetupalli SR, Ganapathy S. Towards sound based testing of COVID-19-Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge. COMPUT SPEECH LANG 2021; 73:101320. [PMID: 34840419 PMCID: PMC8610834 DOI: 10.1016/j.csl.2021.101320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/20/2021] [Accepted: 10/28/2021] [Indexed: 11/30/2022]
Abstract
The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the “Coswara” project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled “Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge”. The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.
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Affiliation(s)
- Neeraj Kumar Sharma
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Ananya Muguli
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Prashant Krishnan
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Rohit Kumar
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Srikanth Raj Chetupalli
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Sriram Ganapathy
- Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India
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Pahar M, Klopper M, Reeve B, Warren R, Theron G, Niesler T. Automatic cough classification for tuberculosis screening in a real-world environment. Physiol Meas 2021; 42. [PMID: 34649231 DOI: 10.1088/1361-6579/ac2fb8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/14/2021] [Indexed: 11/12/2022]
Abstract
Objective.The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.Approach.We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines, k-nearest neighbour, multilayer perceptrons and convolutional neural networks.Main Results.Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection, our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients. This system achieves a sensitivity of 93% at a specificity of 95% and thus exceeds the 90% sensitivity at 70% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test.Significance.The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden.
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Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa
| | - Marisa Klopper
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Byron Reeve
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Rob Warren
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Grant Theron
- SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, DSI/NRF Centre of Excellence for Biomedical Tuberculosis Research, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa
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Soltanian M, Borna K. Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network. Biomed Signal Process Control 2021; 72:103333. [PMID: 34804190 PMCID: PMC8590951 DOI: 10.1016/j.bspc.2021.103333] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 10/22/2021] [Accepted: 11/02/2021] [Indexed: 11/25/2022]
Abstract
Automatic classification of cough data can play a vital role in early detection of Covid-19. Lots of Covid-19 symptoms are somehow related to the human respiratory system, which affect sound production organs. As a result, anomalies in cough sound is expected to be discovered in Covid-19 patients as a sign of infection. This drives the research towards detection of potential Covid-19 cases with inspecting cough sound. While there are several well-performing deep networks, which are capable of classifying sound with a high accuracy, they are not suitable for using in early detection of Covid-19 as they are huge and power/memory hungry. Actually, cough recognition algorithms need to be implemented in hand-held or wearable devices in order to generate early Covid-19 warning without the need to refer individuals to health centers. Therefore, accurate and at the same time lightweight classifiers are needed, in practice. So, there is a need to either compress the complicated models or design light-weight models from the beginning which are suitable for implementation on embedded devices. In this paper, we follow the second approach. We investigate a new lightweight deep learning model to distinguish Covid and Non-Covid cough data. This model not only achieves the state of the art on the well-known and publicly available Virufy dataset, but also is shown to be a good candidate for implementation in low-power devices suitable for hand-held applications.
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Affiliation(s)
- Mohammad Soltanian
- Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran
| | - Keivan Borna
- Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran
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Despotovic V, Ismael M, Cornil M, Call RM, Fagherazzi G. Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results. Comput Biol Med 2021; 138:104944. [PMID: 34656870 PMCID: PMC8513517 DOI: 10.1016/j.compbiomed.2021.104944] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 10/04/2021] [Accepted: 10/11/2021] [Indexed: 12/16/2022]
Abstract
COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.
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Affiliation(s)
- Vladimir Despotovic
- University of Luxembourg, Department of Computer Science, Esch-sur-Alzette, Luxembourg,Corresponding author
| | - Muhannad Ismael
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Maël Cornil
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Roderick Mc Call
- Luxembourg Institute of Science and Technology, IT for Innovation in Services Department, Esch-sur-Alzette, Luxembourg
| | - Guy Fagherazzi
- Luxembourg Institute of Health, Department of Population Health, Deep Digital Phenotyping Research Unit, Strassen, Luxembourg
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Chung Y, Jin J, Jo HI, Lee H, Kim SH, Chung SJ, Yoon HJ, Park J, Jeon JY. Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI. SENSORS 2021; 21:s21217036. [PMID: 34770341 PMCID: PMC8586978 DOI: 10.3390/s21217036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/21/2021] [Accepted: 10/21/2021] [Indexed: 11/28/2022]
Abstract
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
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Affiliation(s)
- Youngbeen Chung
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Jie Jin
- School of Electromechanical and Automotive Engineering, Yantai University, 30 Qingquan Road, Laishan District, Yantai 264005, China;
| | - Hyun In Jo
- Department of Architectural Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Hyun Lee
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Sang-Heon Kim
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Sung Jun Chung
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Ho Joo Yoon
- Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea; (H.L.); (S.J.C.); (H.J.Y.)
| | - Junhong Park
- Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
- Correspondence: (S.-H.K.); (J.P.); Tel.: +82-02-2220-8336 (S.-H.K.); +82-02-2220-0424 (J.P.)
| | - Jin Yong Jeon
- Department of Medical and Digital Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea;
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Al-Dhlan KA. An adaptive speech signal processing for COVID-19 detection using deep learning approach. INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2021; 25:641-649. [PMID: 34456611 PMCID: PMC8380014 DOI: 10.1007/s10772-021-09878-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 07/29/2021] [Indexed: 06/13/2023]
Abstract
Researchers and scientists have been conducting plenty of research on COVID-19 since its outbreak. Healthcare professionals, laboratory technicians, and front-line workers like sanitary workers, data collectors are putting tremendous efforts to avoid the prevalence of the COVID-19 pandemic. Currently, the reverse transcription polymerase chain reaction (RT-PCR) testing strategy determines the COVID-19 virus. This RT-PCR processing is more expensive and induces violation of social distancing rules, and time-consuming. Therefore, this research work introduces generative adversarial network deep learning for quickly detect COVID-19 from speech signals. This proposed system consists of two stages, pre-processing and classification. This work uses the least mean square (LMS) filter algorithm to remove the noise or artifacts from input speech signals. After removing the noise, the proposed generative adversarial network classification method analyses the mel-frequency cepstral coefficients features and classifies the COVID-19 signals and non-COVID-19 signals. The results show a more prominent correlation of MFCCs with various COVID-19 cough and breathing sounds, while the sound is more robust between COVID-19 and non-COVID-19 models. As compared with the existing Artificial Neural Network, Convolutional Neural Network, and Recurrent Neural Network, the proposed GAN method obtains the best result. The precision, recall, accuracy, and F-measure of the proposed GAN are 96.54%, 96.15%, 98.56%, and 0.96, respectively.
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Affiliation(s)
- Kawther A. Al-Dhlan
- Information and Computer Science Department, University of Ha’il, Hail, Kingdom of Saudi Arabia
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Balamurali BT, Hee HI, Kapoor S, Teoh OH, Teng SS, Lee KP, Herremans D, Chen JM. Deep Neural Network-Based Respiratory Pathology Classification Using Cough Sounds. SENSORS (BASEL, SWITZERLAND) 2021; 21:5555. [PMID: 34450996 PMCID: PMC8402243 DOI: 10.3390/s21165555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022]
Abstract
Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). To train a deep neural network model, we collected a new dataset of cough sounds, labelled with a clinician's diagnosis. The chosen model is a bidirectional long-short-term memory network (BiLSTM) based on Mel-Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs-healthy or pathology (in general or belonging to a specific respiratory pathology)-reaches accuracy exceeding 84% when classifying the cough to the label provided by the physicians' diagnosis. To classify the subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among four classes of coughs, overall accuracy dropped: one class of pathological coughs is often misclassified as the other. However, if one considers the healthy cough classified as healthy and pathological cough classified to have some kind of pathology, then the overall accuracy of the four-class model is above 84%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological coughs, irrespective of the underlying conditions, occupy the same feature space making it harder to differentiate only using MFCC features.
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Affiliation(s)
- B T Balamurali
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Hwan Ing Hee
- Department of Paediatric Anaesthesia, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
- Anaesthesiology and Perioperative Sciences, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
| | - Saumitra Kapoor
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
| | - Oon Hoe Teoh
- Respiratory Medicine Service, Department of Paediatrics, KK Women’s and Children’s Hospital, Singapore 229899, Singapore;
| | - Sung Shin Teng
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Khai Pin Lee
- Department of Emergency Medicine, KK Women’s and Children’s Hospital, Singapore 229899, Singapore; (S.S.T.); (K.P.L.)
| | - Dorien Herremans
- Information Systems, Technology, and Design, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Jer Ming Chen
- Science, Mathematics and Technology, Singapore University of Technology and Design, Singapore 487372, Singapore; (S.K.); (J.M.C.)
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Mohammed EA, Keyhani M, Sanati-Nezhad A, Hejazi SH, Far BH. An ensemble learning approach to digital corona virus preliminary screening from cough sounds. Sci Rep 2021; 11:15404. [PMID: 34321592 PMCID: PMC8319422 DOI: 10.1038/s41598-021-95042-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 07/09/2021] [Indexed: 02/05/2023] Open
Abstract
This work develops a robust classifier for a COVID-19 pre-screening model from crowdsourced cough sound data. The crowdsourced cough recordings contain a variable number of coughs, with some input sound files more informative than the others. Accurate detection of COVID-19 from the sound datasets requires overcoming two main challenges (i) the variable number of coughs in each recording and (ii) the low number of COVID-positive cases compared to healthy coughs in the data. We use two open datasets of crowdsourced cough recordings and segment each cough recording into non-overlapping coughs. The segmentation enriches the original data without oversampling by splitting the original cough sound files into non-overlapping segments. Splitting the sound files enables us to increase the samples of the minority class (COVID-19) without changing the feature distribution of the COVID-19 samples resulted from applying oversampling techniques. Each cough sound segment is transformed into six image representations for further analyses. We conduct extensive experiments with shallow machine learning, Convolutional Neural Network (CNN), and pre-trained CNN models. The results of our models were compared to other recently published papers that apply machine learning to cough sound data for COVID-19 detection. Our method demonstrated a high performance using an ensemble model on the testing dataset with area under receiver operating characteristics curve = 0.77, precision = 0.80, recall = 0.71, F1 measure = 0.75, and Kappa = 0.53. The results show an improvement in the prediction accuracy of our COVID-19 pre-screening model compared to the other models.
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Affiliation(s)
- Emad A Mohammed
- Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, Canada
| | - Mohammad Keyhani
- Haskayne School of Business, University of Calgary, Calgary, T2N 1N4, Canada
| | - Amir Sanati-Nezhad
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, T2N 1N4, Canada
| | - S Hossein Hejazi
- Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, T2N 1N4, Canada.
| | - Behrouz H Far
- Department of Electrical and Software Engineering, University of Calgary, Calgary, T2N 1N4, Canada.
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Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM, Alghamdi WM, Alshahrani MS. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:102327-102344. [PMID: 34786317 PMCID: PMC8545201 DOI: 10.1109/access.2021.3097559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 06/02/2023]
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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Affiliation(s)
- Kawther S. Alqudaihi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nida Aslam
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Irfan Ullah Khan
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Abdullah M. Almuhaideb
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Shikah J. Alsunaidi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nehad M. Abdel Rahman Ibrahim
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fahd A. Alhaidari
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fatema S. Shaikh
- Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Yasmine M. Alsenbel
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Dima M. Alalharith
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Hajar M. Alharthi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Wejdan M. Alghamdi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Mohammed S. Alshahrani
- Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
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Pahar M, Klopper M, Warren R, Niesler T. COVID-19 cough classification using machine learning and global smartphone recordings. Comput Biol Med 2021; 135:104572. [PMID: 34182331 PMCID: PMC8213969 DOI: 10.1016/j.compbiomed.2021.104572] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022]
Abstract
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-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (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.
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Affiliation(s)
- Madhurananda Pahar
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa.
| | - Marisa Klopper
- SAMRC Centre for Tuberculosis Research, DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa.
| | - Robin Warren
- SAMRC Centre for Tuberculosis Research, DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa.
| | - Thomas Niesler
- Department of Electrical and Electronic Engineering, Stellenbosch University, South Africa.
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Laguarta J, Hueto F, Subirana B. COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2020; 1:275-281. [PMID: 34812418 PMCID: PMC8545024 DOI: 10.1109/ojemb.2020.3026928] [Citation(s) in RCA: 190] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/31/2020] [Accepted: 09/21/2020] [Indexed: 11/18/2022] Open
Abstract
Goal: We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. Methods: We developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings, and provide a personalized patient saliency map to longitudinally monitor patients in real-time, non-invasively, and at essentially zero variable cost. Cough recordings are transformed with Mel Frequency Cepstral Coefficient and inputted into a Convolutional Neural Network (CNN) based architecture made up of one Poisson biomarker layer and 3 pre-trained ResNet50's in parallel, outputting a binary pre-screening diagnostic. Our CNN-based models have been trained on 4256 subjects and tested on the remaining 1064 subjects of our dataset. Transfer learning was used to learn biomarker features on larger datasets, previously successfully tested in our Lab on Alzheimer's, which significantly improves the COVID-19 discrimination accuracy of our architecture. Results: When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). For asymptomatic subjects it achieves sensitivity of 100% with a specificity of 83.2%. Conclusions: AI techniques can produce a free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 asymptomatic screening tool to augment current approaches in containing the spread of COVID-19. Practical use cases could be for daily screening of students, workers, and public as schools, jobs, and transport reopen, or for pool testing to quickly alert of outbreaks in groups. General speech biomarkers may exist that cover several disease categories, as we demonstrated using the same ones for COVID-19 and Alzheimer's.
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Affiliation(s)
| | - Ferran Hueto
- MIT AutoID LaboratoryCambridgeMA02139USA
- Harvard UniversityCambridgeMA02138USA
| | - Brian Subirana
- MIT AutoID LaboratoryCambridgeMA02139USA
- Harvard UniversityCambridgeMA02138USA
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Zürcher K, Morrow C, Riou J, Ballif M, Koch AS, Bertschinger S, Liu X, Sharma M, Middelkoop K, Warner D, Wood R, Egger M, Fenner L. Novel approach to estimate tuberculosis transmission in primary care clinics in sub-Saharan Africa: protocol of a prospective study. BMJ Open 2020; 10:e036214. [PMID: 32847906 PMCID: PMC7451471 DOI: 10.1136/bmjopen-2019-036214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION Tuberculosis (TB) transmission is difficult to measure, and its drivers are not well understood. The effectiveness of infection control measures at healthcare clinics and the most appropriate intervention strategies to interrupt transmission are unclear. We propose a novel approach using clinical, environmental and position-tracking data to study the risk of TB transmission at primary care clinics in TB and HIV high burden settings in sub-Saharan Africa. METHODS AND ANALYSIS We describe a novel and rapid study design to assess risk factors for airborne TB transmission at primary care clinics in high-burden settings. The study protocol combines a range of different measurements. We will collect anonymous data on the number of patients, waiting times and patient movements using video sensors. Also, we will collect acoustic sound recordings to determine the frequency and intensity of coughing. Environmental data will include indoor carbon dioxide levels (CO2 in parts per million) and relative humidity. We will also extract routinely collected clinical data from the clinic records. The number of Mycobacterium tuberculosis particles in the air will be ascertained from dried filter units using highly sensitive digital droplet PCR. We will calculate rebreathed air volume based on people density and CO2 levels and develop a mathematical model to estimate the risk of TB transmission. The mathematical model can then be used to estimate the effect of possible interventions such as separating patient flows or improving ventilation in reducing transmission. The feasibility of our approach was recently demonstrated in a pilot study in a primary care clinic in Cape Town, South Africa. ETHICS AND DISSEMINATION The study was approved by the University of Cape Town (HREC/REF no. 228/2019), the City of Cape Town (ID-8139) and the Ethics Committee of the Canton Bern (2019-02131), Switzerland. The results will be disseminated in international peer-reviewed journals.
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Affiliation(s)
- Kathrin Zürcher
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Carl Morrow
- Desmond Tutu HIV Centre, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine and the Department of Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Julien Riou
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Marie Ballif
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
| | - Anastasia Sideris Koch
- Institute of Infectious Disease and Molecular Medicine and the Department of Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
- Molecular Mycobacteriology Research Unit, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Simon Bertschinger
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
- Medical Informatics, Berne University of Applied Sciences, Bern, Switzerland
| | - Xin Liu
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA
| | - Manuja Sharma
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA
| | - Keren Middelkoop
- Desmond Tutu HIV Centre, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine and the Department of Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Digby Warner
- Institute of Infectious Disease and Molecular Medicine and the Department of Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
- Molecular Mycobacteriology Research Unit, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Robin Wood
- Desmond Tutu HIV Centre, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine and the Department of Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Matthias Egger
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
- Centre for Infectious Disease Epidemiology & Research, School of Public Health & Family Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Lukas Fenner
- Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland
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Soliński M, Łepek M, Kołtowski Ł. Automatic cough detection based on airflow signals for portable spirometry system. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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Methods of Cough Assessment. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2019; 7:1715-1723. [DOI: 10.1016/j.jaip.2019.01.049] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/28/2019] [Accepted: 01/29/2019] [Indexed: 12/24/2022]
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Turner RD. Cough in pulmonary tuberculosis: Existing knowledge and general insights. Pulm Pharmacol Ther 2019; 55:89-94. [PMID: 30716411 DOI: 10.1016/j.pupt.2019.01.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 01/27/2019] [Accepted: 01/30/2019] [Indexed: 12/14/2022]
Abstract
Cough is a prominent symptom of pulmonary tuberculosis (TB), one of the oldest and most prevalent infectious diseases. Coughing probably has a pivotal role in transmission of the causative organism Mycobacterium tuberculosis. Despite this, little research to date has addressed this subject. Current knowledge of the mechanisms of cough in TB and how exactly coughing patterns predict infectiousness is scant, but this is changing. This overview summarises the existing evidence for the infectiousness of cough in TB, clinical correlates, and possible causes of cough in TB. Potential unique characteristics of cough in the disease are discussed, as is treatment and the subjective awareness of coughing in the disease.
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